米波雷达阵列超分辨和测高方法研究
|
摘要
阵列信号处理是现代信号处理的一个重要研究分支,其应用涉及到雷达、通信以及声呐等多个领域。波达方向(DOA: direction of arrival)估计是阵列信号处理中最主要的研究方向,随着近三十年阵列超分辨技术的发展,DOA估计得到了广泛的关注和快速的发展。但是受实际复杂环境和地形的影响,很多超分辨算法无法在实际系统中取得令人满意的结果,因此估计精度高、易于实现、稳健的DOA估计算法就成为广大学者的研究目标。
米波雷达波长较长,具有良好的反隐身和对抗反辐射导弹的能力,且作用距离远,衰减比微波波段要小。但是,受天线尺寸的限制,米波雷达波束宽,角分辨率较差,天线副瓣高,抗干扰能力差等缺点比较明显,特别是俯仰上波束打地,仰角测量精度受到地面(或海面)多径反射的影响较为严重,从而影响了米波雷达对目标高度的准确测量。因此,米波雷达测高一直是雷达界一大难题。本文以米波雷达低仰角超分辨为主要出发点,结合米波三坐标雷达课题,围绕低复杂度、解相关和高精度等目标展开工作,具体工作概括如下:
1.针对传统算法对多径信号的DOA估计自适应性差和特征值分解运算量大的问题,研究了一种基于实值特征子空间迭代的DOA估计算法。对每个快拍回波数据,通过梯度算法自适应更新特征子空间,并将复值特征子空间变换为实值以实现多径信号的解相关,最后利用基于信号子空间的Unitary ESPRIT算法或噪声子空间的实值求根MUSIC算法进行角度估计。仿真实验和实测数据处理结果表明,由于引入了自适应特征子空间迭代和实值域处理,该算法无需特征值分解,具有较低的运算量和良好的自适应性,可应用于多径信号的角度估计问题。
2.研究了基于行列合成处理和超分辨测角的双迭代方法。在平面阵列进行波达方向估计时,通常会涉及较多的阵元空间和二维角度搜索,基于此,在单目标存在反射多径的模型下,引入行列合成处理实现降维,并结合实值Root-MUSIC算法和RELAX算法完成角度估计。首先,通过构造行、列合成因子将面阵数据矩阵降维为两个线阵的数据向量,由实值Root-MUSIC算法或RELAX算法分维进行角度估计,进而由测角结果重构合成因子,行列合成和角度估计的过程反复迭代、直至收敛。双迭代过程逐渐降低了每次迭代时合成因子的误差并提高了测角精度,在保持二维角度估计精度的前提下,显著地降低了传统方法的运算量,并由实测数据处理结果得到了验证。
3.研究了基于二维空间平滑的波束域MUSIC算法。在均匀线阵空间平滑解相关的原理上,提出了基于均匀面阵的解相关处理方法—二维空间平滑算法。沿面阵的两维方向进行二维空间平滑实现相关信号的解相关,然后将空间平滑后阵元域的数据变换到维数显著降低的波束域,利用波束域MUSIC算法估计相关信号的二维角度。该方法能有效地对多个相关信号进行解相关,在降低传统高分辨算法运算量的同时,可以获得比阵元空间更加稳健的测角性能。
4.基于DFT的稀疏分析通过迭代方法最小化约束条件下的代价函数,增强信号成分、压低噪声来获得比传统的DBF方法更高的分辨率。将稀疏分析应用于角度估计中,对阵列信号的长度进行了虚拟拓展,突破了阵列分辨率的瑞利限,能获得与MUSIC算法相当的测角精度,且无需特征值分解和解相关处理。同时,将稀疏分析应用于空时二维参数的估计中,时域稀疏解用于估计信号的频率并分离信号,对分离后的信号分别进行空域稀疏分析,得到信号的波达方向估计。最后提出了基于均匀面阵稀疏分析的二维波达方向估计方法,依次沿面阵方位向和俯仰向进行稀疏分析,分离信号并得到每个信号的二维角度;针对算法存在测角盲区的问题,给出了一种改进方法,通过求解空间二维稀疏解得到二维角度估计。
5.研究了具有双尺度空间旋转不变性的稀疏阵列。分析了阵列的双尺度旋转不变性,然后使用Unitary ESPRIT算法进行测角,第一个具有半波长平移的旋转不变性得到空间角频率的无模糊粗估计;第二个具有远大于半波长平移的旋转不变性得到循环模糊的精估计,用粗估计结果对精估计解模糊获得无模糊的精估计。提出了一种基于旋转矩阵特征值等价性的自动配对准则,有效地解决了多目标时,方位角、仰角、粗估计及精估计可能出现的失配问题。和常规阵列相比,稀疏阵列的计算复杂度并没有增加,而大的阵列孔径保证了其较高的测角精度和分辨率。
6.研究了基于精确多径信号模型的合成导向矢量测高方法。综合考虑反射系数和多径反射波与直达波的波程差产生的相位等因素,研究了基于地形参数—角度二维搜索的合成导向矢量MUSIC和ML算法;并提出利用二次雷达信息计算目标反射点与天线中心的高度差,避免了地形参数的测量,并且将二维搜索简化为一维角度搜索。另外,给出了一种由测角误差自适应调整地形参数的免搜索方法的可行性分析。结合某米波三坐标雷达实测数据,对所提方法进行了验证,结果表明,对较平坦和起伏不大的阵地,这种合成导向矢量方法可以获得较高的测高精度。
As an important research branch in modern signal processing, array signal processing has applied to many fields, such as radar, communications and sonar. Direction-of-arrival(DOA) is the most important research field in array signal processing, and has received considerable attention with the increasing development of array signal processing technique in the past three decades. Affected by the complex environment and rugged terrain, few super-resolution algorithms can obtain satisfying results, DOA estimation algorithms with high accuracy, suitable engineering realization and robustness become the objective to many researchers.
As a kind of meter-wave radar, it has good anti-stealth effect, anti-arm capacity, less attenuation compared with microwave radar, and large detection range. The meter-wave radar has more great inspection ability on slow velocity target. Limited by aperture, meter-wave radar has wide beam, which usually causes bad angular resolution.
Furthermore, high sidelobe excludes good anti-interference capacity. Especially when the beam irradiates on the ground, the direct path echo comes into the mainlobe as well as the reflection multipath echo, in which case the measurement accuracy of elevation deteriorates sharply. This dissertation starts from low angle estimation in meter-wave radar, mainly discusses low complexity, de-correlation and high estimation accuracy. The main content of this dissertation is summarized as follows.
1. A novel method for DOAs estimation of multipath signals is studied to enhance the adaptability of the traditional high-resolution methods and to reduce the computational burden caused by eigen-decomposition procedure. The eigen-subspace is iteratively obtained by gradient algorithm for each snapshot, and then it is transformed from complex-valued space to real-valued space. Finally, Unitary ESPRIT or Root-MUSIC based on real-valued space is used to estimate the angles. Simulations and real data processing results show that, introduction of the eigen-subspace iteration and real-valued space processing makes the algorithm suitable for DOAs estimation of multipath signals without eigen-decomposition, thus reducing the computational complexity significantly.
2. Chapter 3 focuses on dual-iteration algorithm based on row-column synthesizing and angle estimation with high-resolution algorithms. The traditional high-resolution methods for 2D DOA estimation using uniform rectangular array usually involve eigen-decomposition and multi-dimensional spectral search, which are procedures with high computational complexity. For single target with reflection multipath, row-column synthesizing is introduced to decrease the dimension, and real-valued 2D Root-MUSIC and RELAX are combined to estimate the DOA. Starting with some initial 2D angles, this method synthesizes the received data of planar array into vectors along both the row and column directions. Then real-valued Root-MUSIC or RELAX is used to obtain the elevation and azimuth angles respectively. Then, the weighted vectors for row-column synthesizing are reconstructed using the estimated 2D angles, and the 2D angles are obtained iteratively until they reach the given precision. The dual-iteration algorithm not only reduces the error of weighted vectors, but also improves estimation accuracy iteratively. This method is a simple procedure with a high convergence rate, and it has lower computation burden and comparable accuracy compared with the traditional processing methods, which are validated by real data processing results.
3. Beamspace MUSIC method based on two-dimensional spatial smoothing is discussed. On the basis of spatial smoothing for uniform linear array, a de-correlation method for uniform rectangular array is presented. Spatial smoothing is implemented along both dimensions firstly to de-correlate the coherent signals. Then the de-correlated array data in element space is transformed to that in beamspace with much smaller size. Finally, 2D angles of coherent signals are obtained by virtue of beamspace MUSIC. The novel method not only de-correlates multiple coherent signals effectively, but obtains more robust estimation properties with much lower computational burden compared with the traditional high-resolution methods.
4. Making use of iteration algorithm, sparse analysis based on discrete Fourier transform(DFT) gradually reinforces signal component while suppressing noise component by solving the objective function under some constraint, thus improving the resolution compared with digital beamforming(DBF). When applied to DOA estimation, the sparse analysis generates sparse solution corresponding to the DFT of the extrapolated data for a longer array, which can achieves resolution beyond Rayleigh limit. The analysis shows that, without eigen-decomposition and de-correlation, sparse analysis obtains comparable estimation accuracy with MUSIC. To spatial-temporal parameters estimation, sparse analysis carries out temporal sparse solution to get the frequency estimates and to separate the signals of different frequencies, then generates spatial sparse solution of each separated signals to enhance the spatial resolution and obtain DOA. Finally, a novel method for 2D DOA estimation based on sparse analysis using uniform rectangular array is proposed. Sparse solutions along azimuth direction and elevation direction are obtained in turn, then the solutions with different angular frequencies can be separated, 2D DOA can be estimated from each separated sparse solution. A modified method is presented to overcome the blind angular region problem occurred in the algorithm.
5. A sparse planar array with two sizes of spatial invariances along both dimensions is analyzed to improve the angle estimation accuracy of sources in low angle region with reflection multipath. With two sizes of spatial invariances along both dimensions, the array estimates the azimuth and elevation angles using Unitary ESPRIT. The first spatial invariance with a displacement of half-wavelength yields unambiguous coarse estimates of high-variance, while the second spatial invariance with much larger a displacement of subarrays obtains cyclically ambiguous fine estimates of low-variance. The final estimates are obtained by disambiguating fine estimates with coarse estimates. A novel pairing scheme is proposed to pair both the azimuth and elevation, coarse and fine angle estimates of the same signal. Employing real-valued processing and array aperture extension, the new DOA measuring method de-correlates the multipath signals and enhances angle resolution with no extra antennas and computational complexity.
6. A synthetic steering vector altitude measurement method based on highly refined multipath signal is studied. Taking reflection coefficient and phase difference between the two paths into consideration, we analyze synthetic steering vector MUSIC and ML algorithm based on 2D search on terrain parameter and angle. Based on second radar, a method calculating terrain parameter is proposed, which avoids actual measurement of terrain parameter and simplifies 2D search procedure to 1D angular search. Finally, an adaptive method which uses angular measurement error to adjust terrain parameters is analyzed, avoiding search procedure in calculating terrain parameter. The real data collected by some VHF radar demonstrates the validity and feasibility of the proposed methods.
米波雷达波长较长,具有良好的反隐身和对抗反辐射导弹的能力,且作用距离远,衰减比微波波段要小。但是,受天线尺寸的限制,米波雷达波束宽,角分辨率较差,天线副瓣高,抗干扰能力差等缺点比较明显,特别是俯仰上波束打地,仰角测量精度受到地面(或海面)多径反射的影响较为严重,从而影响了米波雷达对目标高度的准确测量。因此,米波雷达测高一直是雷达界一大难题。本文以米波雷达低仰角超分辨为主要出发点,结合米波三坐标雷达课题,围绕低复杂度、解相关和高精度等目标展开工作,具体工作概括如下:
1.针对传统算法对多径信号的DOA估计自适应性差和特征值分解运算量大的问题,研究了一种基于实值特征子空间迭代的DOA估计算法。对每个快拍回波数据,通过梯度算法自适应更新特征子空间,并将复值特征子空间变换为实值以实现多径信号的解相关,最后利用基于信号子空间的Unitary ESPRIT算法或噪声子空间的实值求根MUSIC算法进行角度估计。仿真实验和实测数据处理结果表明,由于引入了自适应特征子空间迭代和实值域处理,该算法无需特征值分解,具有较低的运算量和良好的自适应性,可应用于多径信号的角度估计问题。
2.研究了基于行列合成处理和超分辨测角的双迭代方法。在平面阵列进行波达方向估计时,通常会涉及较多的阵元空间和二维角度搜索,基于此,在单目标存在反射多径的模型下,引入行列合成处理实现降维,并结合实值Root-MUSIC算法和RELAX算法完成角度估计。首先,通过构造行、列合成因子将面阵数据矩阵降维为两个线阵的数据向量,由实值Root-MUSIC算法或RELAX算法分维进行角度估计,进而由测角结果重构合成因子,行列合成和角度估计的过程反复迭代、直至收敛。双迭代过程逐渐降低了每次迭代时合成因子的误差并提高了测角精度,在保持二维角度估计精度的前提下,显著地降低了传统方法的运算量,并由实测数据处理结果得到了验证。
3.研究了基于二维空间平滑的波束域MUSIC算法。在均匀线阵空间平滑解相关的原理上,提出了基于均匀面阵的解相关处理方法—二维空间平滑算法。沿面阵的两维方向进行二维空间平滑实现相关信号的解相关,然后将空间平滑后阵元域的数据变换到维数显著降低的波束域,利用波束域MUSIC算法估计相关信号的二维角度。该方法能有效地对多个相关信号进行解相关,在降低传统高分辨算法运算量的同时,可以获得比阵元空间更加稳健的测角性能。
4.基于DFT的稀疏分析通过迭代方法最小化约束条件下的代价函数,增强信号成分、压低噪声来获得比传统的DBF方法更高的分辨率。将稀疏分析应用于角度估计中,对阵列信号的长度进行了虚拟拓展,突破了阵列分辨率的瑞利限,能获得与MUSIC算法相当的测角精度,且无需特征值分解和解相关处理。同时,将稀疏分析应用于空时二维参数的估计中,时域稀疏解用于估计信号的频率并分离信号,对分离后的信号分别进行空域稀疏分析,得到信号的波达方向估计。最后提出了基于均匀面阵稀疏分析的二维波达方向估计方法,依次沿面阵方位向和俯仰向进行稀疏分析,分离信号并得到每个信号的二维角度;针对算法存在测角盲区的问题,给出了一种改进方法,通过求解空间二维稀疏解得到二维角度估计。
5.研究了具有双尺度空间旋转不变性的稀疏阵列。分析了阵列的双尺度旋转不变性,然后使用Unitary ESPRIT算法进行测角,第一个具有半波长平移的旋转不变性得到空间角频率的无模糊粗估计;第二个具有远大于半波长平移的旋转不变性得到循环模糊的精估计,用粗估计结果对精估计解模糊获得无模糊的精估计。提出了一种基于旋转矩阵特征值等价性的自动配对准则,有效地解决了多目标时,方位角、仰角、粗估计及精估计可能出现的失配问题。和常规阵列相比,稀疏阵列的计算复杂度并没有增加,而大的阵列孔径保证了其较高的测角精度和分辨率。
6.研究了基于精确多径信号模型的合成导向矢量测高方法。综合考虑反射系数和多径反射波与直达波的波程差产生的相位等因素,研究了基于地形参数—角度二维搜索的合成导向矢量MUSIC和ML算法;并提出利用二次雷达信息计算目标反射点与天线中心的高度差,避免了地形参数的测量,并且将二维搜索简化为一维角度搜索。另外,给出了一种由测角误差自适应调整地形参数的免搜索方法的可行性分析。结合某米波三坐标雷达实测数据,对所提方法进行了验证,结果表明,对较平坦和起伏不大的阵地,这种合成导向矢量方法可以获得较高的测高精度。
As an important research branch in modern signal processing, array signal processing has applied to many fields, such as radar, communications and sonar. Direction-of-arrival(DOA) is the most important research field in array signal processing, and has received considerable attention with the increasing development of array signal processing technique in the past three decades. Affected by the complex environment and rugged terrain, few super-resolution algorithms can obtain satisfying results, DOA estimation algorithms with high accuracy, suitable engineering realization and robustness become the objective to many researchers.
As a kind of meter-wave radar, it has good anti-stealth effect, anti-arm capacity, less attenuation compared with microwave radar, and large detection range. The meter-wave radar has more great inspection ability on slow velocity target. Limited by aperture, meter-wave radar has wide beam, which usually causes bad angular resolution.
Furthermore, high sidelobe excludes good anti-interference capacity. Especially when the beam irradiates on the ground, the direct path echo comes into the mainlobe as well as the reflection multipath echo, in which case the measurement accuracy of elevation deteriorates sharply. This dissertation starts from low angle estimation in meter-wave radar, mainly discusses low complexity, de-correlation and high estimation accuracy. The main content of this dissertation is summarized as follows.
1. A novel method for DOAs estimation of multipath signals is studied to enhance the adaptability of the traditional high-resolution methods and to reduce the computational burden caused by eigen-decomposition procedure. The eigen-subspace is iteratively obtained by gradient algorithm for each snapshot, and then it is transformed from complex-valued space to real-valued space. Finally, Unitary ESPRIT or Root-MUSIC based on real-valued space is used to estimate the angles. Simulations and real data processing results show that, introduction of the eigen-subspace iteration and real-valued space processing makes the algorithm suitable for DOAs estimation of multipath signals without eigen-decomposition, thus reducing the computational complexity significantly.
2. Chapter 3 focuses on dual-iteration algorithm based on row-column synthesizing and angle estimation with high-resolution algorithms. The traditional high-resolution methods for 2D DOA estimation using uniform rectangular array usually involve eigen-decomposition and multi-dimensional spectral search, which are procedures with high computational complexity. For single target with reflection multipath, row-column synthesizing is introduced to decrease the dimension, and real-valued 2D Root-MUSIC and RELAX are combined to estimate the DOA. Starting with some initial 2D angles, this method synthesizes the received data of planar array into vectors along both the row and column directions. Then real-valued Root-MUSIC or RELAX is used to obtain the elevation and azimuth angles respectively. Then, the weighted vectors for row-column synthesizing are reconstructed using the estimated 2D angles, and the 2D angles are obtained iteratively until they reach the given precision. The dual-iteration algorithm not only reduces the error of weighted vectors, but also improves estimation accuracy iteratively. This method is a simple procedure with a high convergence rate, and it has lower computation burden and comparable accuracy compared with the traditional processing methods, which are validated by real data processing results.
3. Beamspace MUSIC method based on two-dimensional spatial smoothing is discussed. On the basis of spatial smoothing for uniform linear array, a de-correlation method for uniform rectangular array is presented. Spatial smoothing is implemented along both dimensions firstly to de-correlate the coherent signals. Then the de-correlated array data in element space is transformed to that in beamspace with much smaller size. Finally, 2D angles of coherent signals are obtained by virtue of beamspace MUSIC. The novel method not only de-correlates multiple coherent signals effectively, but obtains more robust estimation properties with much lower computational burden compared with the traditional high-resolution methods.
4. Making use of iteration algorithm, sparse analysis based on discrete Fourier transform(DFT) gradually reinforces signal component while suppressing noise component by solving the objective function under some constraint, thus improving the resolution compared with digital beamforming(DBF). When applied to DOA estimation, the sparse analysis generates sparse solution corresponding to the DFT of the extrapolated data for a longer array, which can achieves resolution beyond Rayleigh limit. The analysis shows that, without eigen-decomposition and de-correlation, sparse analysis obtains comparable estimation accuracy with MUSIC. To spatial-temporal parameters estimation, sparse analysis carries out temporal sparse solution to get the frequency estimates and to separate the signals of different frequencies, then generates spatial sparse solution of each separated signals to enhance the spatial resolution and obtain DOA. Finally, a novel method for 2D DOA estimation based on sparse analysis using uniform rectangular array is proposed. Sparse solutions along azimuth direction and elevation direction are obtained in turn, then the solutions with different angular frequencies can be separated, 2D DOA can be estimated from each separated sparse solution. A modified method is presented to overcome the blind angular region problem occurred in the algorithm.
5. A sparse planar array with two sizes of spatial invariances along both dimensions is analyzed to improve the angle estimation accuracy of sources in low angle region with reflection multipath. With two sizes of spatial invariances along both dimensions, the array estimates the azimuth and elevation angles using Unitary ESPRIT. The first spatial invariance with a displacement of half-wavelength yields unambiguous coarse estimates of high-variance, while the second spatial invariance with much larger a displacement of subarrays obtains cyclically ambiguous fine estimates of low-variance. The final estimates are obtained by disambiguating fine estimates with coarse estimates. A novel pairing scheme is proposed to pair both the azimuth and elevation, coarse and fine angle estimates of the same signal. Employing real-valued processing and array aperture extension, the new DOA measuring method de-correlates the multipath signals and enhances angle resolution with no extra antennas and computational complexity.
6. A synthetic steering vector altitude measurement method based on highly refined multipath signal is studied. Taking reflection coefficient and phase difference between the two paths into consideration, we analyze synthetic steering vector MUSIC and ML algorithm based on 2D search on terrain parameter and angle. Based on second radar, a method calculating terrain parameter is proposed, which avoids actual measurement of terrain parameter and simplifies 2D search procedure to 1D angular search. Finally, an adaptive method which uses angular measurement error to adjust terrain parameters is analyzed, avoiding search procedure in calculating terrain parameter. The real data collected by some VHF radar demonstrates the validity and feasibility of the proposed methods.
引文
[1] Barton D K. Low-angle radar tracking[J]. Proceedings of the IEEE, 1974, 62(6): 587-704.
[2] White W D. Low-angle radar tracking in the presence of multipath[J]. IEEE Trans. on Aerospace and Electronic Systems, 1974, 10(6): 835-852.
[3] Giuli D and Tiberio R. A midified monopulse technique for radar tracking with low-angle multipath[J]. IEEE Trans. on Aerospace and Electronic Systems, 1975, 11(5): 741-748.
[4] Mrstik A V and Smith P G. Multipath limitations on low-angle radar tracking[J]. IEEE Trans. on Aerospace and Electronic Systems, 1978, 14(1): 85-102.
[5] Reilly J, Litva J, and Bauman P. New angle-of-arrival estimator: comparative evaluation applied to the low-angle tracking problem[J]. Communications, Radar and Signal Processing, IEE Proceedings F, 1988, 135(5): 408-420.
[6] Sword C K, Simaan M, and Kamen E W. Multiple target angle tracking using sensor array outputs[J]. IEEE Trans. on Aerospace and Electronic Systems, 1990, 26(2): 367-373.
[7] Bruder J A and Saffold J A. Multipath effects on low-angle tracking at millimeter-wave frequencies[J]. IEE Proceedings F, 1991, 138(2): 172-184.
[8] Zoltowski M D and Lee T-S. Maximum likelihood based sensor array signal processing in the beamspace domain for low angle radar tracking[J]. IEEE Trans. on Signal Processing, 1991, 39(3): 656-671.
[9] Rahamim D, Tabrikian J, and Shavit R. Source localization using vector sensor array in a multipath environment[J]. IEEE Trans. on Signal Processing, 2004, 52(11): 3096-3103.
[10] Hurtado M and Nehorai A. Performance analysis of passive low-grazing-angle source localization in maritime environments using vector sensors[J]. IEEE Trans on Aerospace and Electronic Systems, 2007, 43(2): 780-789.
[11]李金梁,李永祯,王雪松.米波极化雷达的反隐身研究[J].雷达科学与技术, 2005, 3(6): 321-326.
[12]万山虎,丁建江.提高米波雷达四抗性能的关键技术[J].中国雷达, 1998, 2: 1-4.
[13]董智文,屈晓光.防空制导雷达反隐身性能分析[C].第十届全国雷达学术年会, 2008: 376-379.
[14]陶茀毓.现代米波雷达介绍[J].地面防空武器, 2002, 4: 34-38.
[15]陈长兴,巩林玉,班斐等.米波谐振雷达反隐身技术研究[J].舰船电子对抗, 2009, 32(4): 34-37.
[16]陈知明.机载雷达的隐身波形[J].现代雷达, 2006, 28(9): 24-26.
[17]王湃,夏明耀,周乐柱,董硕.不同方法分析隐身飞机的散射特性比较[J].电波科学学报, 2004, 19: 69-72.
[18]石晓宁.米波雷达目标跟踪之分析[J].现代电子, 2000, 73(4): 8-12.
[19]盛景泰.米波雷达多径测高技术[J].中国电子科学研究院学报, 2008, 3(5): 510-514.
[20]胡坤娇,吴剑旗.米波雷达低仰角测高方法试验研究[J].第十届全国雷达学术年会, 2008: 63-66.
[21]胡晓琴,陈建文,陈辉.神经网络用语米波雷达测高[J].空军雷达学院学报, 2004, 18(3): 16-19.
[22]董玫,张守宏,吴向东.距离高分辨测高技术[J].火控雷达技术, 2006, 35(1): 10-14.
[23] Capon J. High-resolution frequency-wavenumber spectrum analysis[J]. IEEE Proceedings, 1969, 57(8): 1408-1418.
[24] Schmidt R O. Multiple emitter location and signal parameter estimation[J]. IEEE Trans. on Antennas and Propagation, 1986, 34(3): 276-280.
[25] Pillai S U and Kwon B H. Forward/backward spatial smoothing techniques for coherent signal identification[J]. IEEE Trans. on Acoustic, Speech and Signal Processing, 1989, 37(1): 8-15.
[26] Rao B D and Hari K V S. Performance analysis of Root-MUSIC[J]. IEEE Trans. on Acoustic, Speech and Signal Processing, 1989, 37(12): 1939-1949.
[27] Roy R and Kailath T. ESPRIT-a subspace rotation approach to estimation of parameters of cissoids in noise[J]. IEEE Trans. on Acoustic, Speech and Signal Processing, 1986, 34(10): 1340-1342.
[28] Haardt M and Nossek J A. Unitary ESPRIT: how to obtain increased estimation accuracy with a reduced computational burden[J]. IEEE Trans. on Signal Processing, 1995, 43(5): 1332-1242.
[29] Stoica P and Sharman K. C. Maximum likehilood methods for direction-of–arrival estimation[J]. IEEE Trans. on Acoustic, Speech and Signal Processing, 1990, 38(7): 1132-1143.
[30] Ziskind I. and Wax M. Maximum likelihood localization of multiple sources by alternating projection[J]. IEEE Trans. on Acoustic, Speech and Signal Processing, 1988, 36(10): 1553-1559.
[31]赵永波,张守宏.米波雷达测高技术研究[R].西安电子科技大学电子工程研究所. 2003.
[32]立平.俄罗斯国家防空系统开始部署米波反隐身数字相控阵雷达[J].航天电子对抗, 2004, 4: 42.
[33] http://www.rusarm.ru/cataloque/air_def/air_def_40-43.pdf
[34] http://www.zgjunshi.com/world/tanke/lujileida/200908/20090804174149.html
[35]保铮,张庆文.一种新型的米波雷达--综合脉冲与孔径雷达[J].现代雷达, 1995, 17(1): 1-13.
[36]张庆文.综合脉冲与孔径雷达系统性能分析与研究[D].西安:西安电子科技大学博士学位论文, 1994.
[37]陈伯孝. SIAR及其四维跟踪处理等技术研究[D].西安:西安电子科技大学博士学位论文, 1997.
[38] Chen Baixiao, Zhang Shouhong, Wu Jianqi, et al. Analysis and experimental results on Sparse-array Synthetic Impulse and Aperture Radar[C]. Proc. of 2006 CIE international conf. on radar, Beijing, China, 2001:76-80.
[39]吴剑旗,贺瑞龙,江凯等.稀布阵综合脉冲孔径雷达的研究与实验[J].现代电子, 1998, 64(3): 1-5.
[40]杨克虎,保铮.多频应用对低角跟踪方向估计性能的改善[J].西安电子科技大学学报, 1993, 20(4): 181-193.
[41]张平,张晓华.一种低仰角跟踪的新方法[J].现代雷达, 2001, 12(6): 9-12.
[42]何子述,黎敏,荆玉兰.频率捷变的相控阵雷达目标多径DOA估计算法[J].系统工程与电子技术, 2005, 27(11): 1880-1882.
[43] Sherman S M.Complex indicated angles applied to unresolved radar targets and multipath[J]. IEEE Trans. on AES, 1971(1): 160-170.
[44] Peyton P Z and Goldman L. Radar performance with multipath using the complex angle[J]. IEEE Trans. on AES, 1971, 7(1): 171-178.
[45] Howard D D, Sherman S M, and Campbell J J. Experimental results of the complex indicated angle techique for multipath correction[J]. IEEE Trans. on AES, 1974, 10(6): 779-787.
[46] Knepp D L. Variance and bias of angle estimation radars[J]. IEEE Trans. on Antennas and Propagation, 1976, 24(4): 518-521.
[47] Zheng Y B, Tseng S M, and Yu K B. Closed-form four-channel monopulse two-target resolution[J]. IEEE Trans. on AES, 2003, 39(3): 1083-1089.
[48] Nickel U and Fhr F. Overview of generalized monopulse estimation[J]. IEEE A&E Systems Magazine, 2006, 21(6): 27-56.
[49]范志杰,尚社.一种用于低空目标探测与跟踪的新方法[J].雷达科学与技术, 2004, 2(3): 153-157.
[50]贾永康,保铮.利用多普勒信息的波达方向最大似然估计方法[J].电子学报, 1997, 25(6): 71-76.
[51]贾永康,保铮.时-空二维信号模型下的波达方向估计方法及其性能分析[J].电子学报, 1997, 25(9): 69-73.
[52]赵光辉,陈伯孝,董玫.基于交替投影的DOA估计方法及其在米波雷达中的应用[J].电子与信息学报, 2008, 30(1): 224-227.
[53]胡铁军,杨雪亚,陈伯孝.阵列内插的波束域ML米波雷达测高方法[J].电波科学学报, 2009, 24(4): 660-666.
[54]吴向东,张守宏,董玫.一种基于线性预处理的米波雷达低仰角处理算法[J].电子学报, 2006, 34(9): 1668-1671.
[55]张文俊,赵永波,张守宏.广义MUSIC算法在米波雷达测高中的应用及其改进[J].电子与信息学报, 2007, 29(2): 387-390.
[56]刘俊,刘峥,刘韵佛.米波雷达仰角和多径衰减系数联合估计算法[J].电子与信息学报, 2011, 33(1): 33-37.
[57]陈伯孝,胡铁军,郑自良等.基于波瓣分裂的米波雷达低仰角测高方法及其应用[J].电子学报, 2007, 35(6): 1021-1025.
[58] Baixiao Chen, Guanghui Zhao and Shouhong Zhang. Altitude measurement based on beam split and frequency diversity in VHF radar[J]. IEEE Trans. on Aerospace and Electronic Systems, 2010, 46(1): 3-13.
[59] Lo T and Litva J. Low-angle tracking using a multifrequency sampled aperture radar[J]. IEEE Trans. on AES, 1991, 27(5): 797-805.
[60] Lo T and Litva J. Use of a highly deterministic multipath signal model in low-angle tracking[J]. IEE Proceedings-F, 1991, 138(2): 163-171.
[61] Lo T, Wong T, and Litva J. New technique for low-angle radar tracking[J]. Electronics Letters, 1991, 27(6): 529-531.
[1] Schmidt R O. Multiple emitter location and signal parameter estimation[J]. IEEE Trans. on Antennas and Propagation, 1986, 34(3): 276-280.
[2] Rao B D and Hari K V S. Performance analysis of Root-MUSIC[J]. IEEE Trans. on Acoustic, Speech and Signal Processing, 1989, 37(12): 1939-1949.
[3] Zlotowski M D, Kautz G M, and Silverstein S D. Beamspace root-MUSIC[J].IEEE Trans. on Signal Processing, 1993, 41(1): 344-364.
[4] Thompson J S, Grant P M, and Mulgrew B. Performance of spatial smoothing algorithms for correlated sources[J]. IEEE Trans. on Signal Processing, 1996, 44(4): 1040-1046.
[5] Al-Ardi E M, Shubair R M, and Al-Mualla M E. Computationally efficient high-resolution DOA estimation in multipath environment[J]. Electronices Letters, 2004, 40(14): 908-910.
[6] Lin J D, Fang W H, Wang Y Y, et al. FSF MUSIC for joint DOA and frequency estimation and its performance analysis[J]. IEEE Trans. on Signal Processing, 2006, 54(12): 4529-4542.
[7] Ying Zhang and Boon Poh Ng. MUSIC-like DOA estimation without estimating the number of sources[J]. IEEE Trans. on Signal Processing, 2010, 58(3): 1668-1676.
[8] Roy R and and Kailath T. ESPRIT- a subspace rotation approach to estimation of parameters of cissoids in noise[J]. IEEE Trans. on Acoustic, Speech and Signal Processing, 1986, 34(10): 1340-1342.
[9] Ottersten B, Viberg M, and Kailath T. Performance analysis of the total least squares ESPRIT algorithm[J]. IEEE Trans. on Signal Processing, 1991, 39(5): 1122-1135.
[10] Haardt M and Nossek J A. Unitary ESPRIT: how to obtain increased estimation accuracy with a reduced computational burden[J]. IEEE Trans. on Signal Processing, 1995, 43(5): 1232-1242.
[11] Xu Guanghan, Silverstein S D, Roy R H., et al. Beamspace ESPRIT[J]. IEEE Trans. on Signal Processing, 1994, 42(2): 349-356.
[12] Lemma A N, Veen A J, and Deprettere E F. Multiresolution ESPRIT algorithm[J]. IEEE Trans. on Signal Processing, 1999, 47(6): 1722-1726.
[13] Gao Feifei and Gershman A B. A generalized ESPRIT approach to direction-of-arrival estimation[J]. IEEE Signal Processing Letters. 2005, 12(3): 254-257.
[14] Owsley N L. Adaptive data orthogonalization[C]. 1978 International Conference on Acoustics, Speech, and Signal Processing. Tulsa: IEEE, 1978: 109-112.
[15] Thompson P A. An adaptive spectral analysis technique for unbiased frequency estimation in the presence of white noise[C]. Proc.13th Asilomar Conf. Circuits, Syst. Cmput., Pacific Grove, CA: CSA, 1980: 529-533.
[16] Durrani T S, and Sharman K C. Eigenfilter approaches to adaptive array processing[J]. IEE Proc. H on Microwaves, Optics and Antennas, 1983, 130(1): 22-28.
[17] Fuhrmann D R and Liu B. Rotational search methods for adaptive pisarenko harmonic retrieval[J]. IEEE Trans. on Acoustics, Speech, and Signal Processing, 1986, 34(6): 1550-1565.
[18] Yang J-F, and Kaveh M. Adaptive eigensubspace algorithms for direction or frequency estimation and tracking[J]. IEEE Trans. on Acoustics, Speech, and Signal Processing, 1988, 36(2): 241-251.
[19] Babu K V S. A fast algorithm for adaptive estimation of root-MUSIC polynomial coefficients[C]. 1991 International Conference on Acoustics, Speech, and Signal Processing. Toronto, Ont.: IEEE, 1991: 2229-2232.
[20] Pesavento M, Gershman A B, and Haardt M. A theoretical and experimental performance study of a root-MUSIC algorithm based on a real-valued eigendecomposition[C]. 2000 International Conference on Istanbul: IEEE, 2000: 3049-3052. Acoustics, Speech, and Signal Processing.
[21] Pesavento M, Gershman A B, and Haardt M. Unitary root-MUSIC with a real-valued eigendecomposition: a theoretical and experimental performance study[J]. IEEE Trans. on Signal Processing, 2000, 48(5): 1306-1314.
[22] Ferreira T N, Netto S L, and Diniz P S R. Convariance-based direction-of-arrival estimation with real structures[J]. IEEE Signal Processing Letters. 2008, 15: 757-760.
[23] Gershman A B and Haardt M. Improving the performance of Unitary ESPRIT via pseudo-noise resampling[J]. IEEE Trans. on Signal Processing, 1999, 47(8): 2305-2308.
[24] Ottersten B, Viberg M, and Kailath T. Performance analysis of the total least squares ESPRIT algorithm[J]. IEEE Trans. on Signal Processing, 1991, 39(5): 1122-1135.
[25] Bachl R. The forward-backward averaging technique applied to TLS-ESPRIT algorithm[J]. IEEE Trans. on Signal Processing, 1991, 43(11): 2691-2699.
[26]杨雪亚,陈伯孝,朱根生.基于实值特征子空间迭代的DOA估计算法[J].西安电子科技大学学报, 2010, 37(2): 242-247.
[27] Zoltowski M D, Haardt M, and Mathews C P. Closed-form 2-D angle estimation with rectangular arrays in element space or beamspace via Unitary ESPRIT[J]. IEEE Trans. on Signal Processing, 1996, 44(2): 316-328.
[28] Zhang Y, Ye Z., Xu X, et al. Estimation of two-dimensional direction-of-arrival for uncorrelated and coherent signals with low complexity[J]. IET Radar Sonar Navig. 2010, 4(4): 507-519.
[1] Pesavento M, Gershman A B, and Haardt M. A theoretical and experimental performance study of a root-MUSIC algorithm based on a real-valued eigendecomposition[C]. 2000 International Conference on Acoustics, Speech, and Signal Processing. Istanbul: IEEE, 2000: 3049-3052.
[2] Pesavento M, Gershman A B, and Haardt M. Unitary root-MUSIC with a real-valued eigendecomposition: a theoretical and experimental performance study[J]. IEEE Trans. on Signal Processing, 2000, 48(5): 1306-1314.
[3] Ferreira T N, Netto S L, and Diniz P S R. Convariance-based direction-of-arrival estimation with real structures[J]. IEEE Signal Processing Letters. 2008, 15: 757-760.
[4] Li J and Stoica P. Efficient mixed-spectrum estimation with applications to target feature extraction[J]. IEEE Trans. on Signal Processing, 1996, 44(2): 281-295.
[5] Li J and Stoica P. Efficient mixed-spectrum estimation with applications to target feature extraction[J]. IEEE Proceedings of ASILOMAR, 1995, 1: 428-432.
[6] Li J, Stoica P, and Zheng Dunmin. Angle and waveform estimation in the presence of colored noise via RELAX[J]. IEEE Proceedings of ASILOMAR, 1995, 1: 433-437.
[7] Jian C, Wang S, and Lin L. Two-dimensional DOA estimation of coherent signals based on 2D Unitary ESPRIT method[C]. The 8th International Conference on Signal Processing. China: Beijing, 2006: 16-20.
[8] Zoltowski M D, Haardt M, and Mathews C P. Closed-form 2-D angle estimation with rectangular arrays in element space or beamspace via Unitary ESPEIT[J]. IEEE Trans. on Signal Processing, 1996, 44(2): 316-328.
[9] Zhang Y, Ye Z, Xu X, et al. Estimation of two-dimensional direction-of-arrival for uncorrelated and coherent signals with low complexity[J]. IET Radar Sonar Navig. 2010, 4(4): 507-519.
[10] Li Jian, Zheng Dunmin, and Stoica P. Angle and waveform estimation via RELAX[J]. IEEE Trans. on Aerospace and Electronic Systems, 1997, 33(3): 1077-1087.
[11] Liu Z-S and Li J. Implementation of the RELAX algorithm[J]. IEEE Transactions on Aerospace and Electronic Systems, 1998, 34(2): 657-664.
[12]邵朝,保铮. RELAX算法的相关域分析[J].西安电子科技大学学报, 1997, 24(2): 164-171.
[13]邵朝,保铮. RELAX算法的分辨特性分析[J].电子科学学刊, 1999, 21(4): 447-454.
[14] Ye X D, Fang D G, Sheng W X, et al. 2-D angle estimation with a uniform circular array via RELAX[C]. International Conference on Computational Electromagnetics and Its Applications, 1999: 175-178.
[15]叶晓东,李尚生,曹东.基于RELAX算法的三维立体线阵方位角俯仰角联合估计[C].第九届全国雷达学术年会, 2004.
[16]苏晋,陈付彬,张军等.基于RELAX谱估计的波束内多运动目标分辨[J].系统工程与电子技术, 2003, 25(11): 1313-1317.
[17]黄晓涛,梁甸农.基于RELAX的UWB-SAR抑制PFI算法[J].国防科技大学学报, 2000, 22(2): 55-59.
[18] Zheng Yiming, Bao Zheng. Autofousing of SAR images based on RELAX[C]. IEEE International Radar Conference, 2000, 533-538.
[19] Vignaud L. Wavelet-RELAX feature extraction in radar images[J]. IEE Proc.-Radar, Sonar and Navigation, 2003, 150(4): 242-246.
[20] Jiao Yun, Yu Jizhou, and Che Renquan. Application of RELAX algorithm to ISAR superresolution imaging[C]. International Conference on Radar, Shanghai, 2006: 1-4.
[21]张超峰,刘丹,程臻.采用RELAX算法提高单脉冲三维成像横向分辨率[J].系统工程与电子技术, 2008, 30(11): 2063-2065.
[22] Zhou Xusheng, Chen Bin, and Sheng Weixing. Application of 2-D RELAX algorithm to SAR superresolution imaging[C]. International Conference on Microwave and Millimeter Wave Technology, 2008: 220-223.
[1] Evans J E, Johnson J R, and Sun D F. High resolution angular spectrum estimation techniques for terrain scattering analysis and angle of arrival estimation[A]. IEEE 1st ASSP workshop spectral estimation, Hamilton, Ontario Canada, 1981: 134-139.
[2] Shan T J, Wax M, and Kailath T. Adaptive beamforming for coherent signals and interference[J]. IEEE Trans. on Acoustics, Speech, and Signal Processing, 1985, 33(3): 527-536.
[3] Shan T J, Wax M, and Kailath T. On spatial smoothing for estimation of coherent signals[J]. IEEE Trans. on Acoustics, Speech, and Signal Processing, 1985, 33(4): 806-811.
[4] Williams R T, Prasad S, Mahalanbis S K, et al. An improved spatial smoothing technique for bearing estimation in a multipath environment[J]. IEEE Trans. on Acoustics, Speech, and Signal Processing, 1988, 36(4): 425-432.
[5] Pillai S U, and Kwon B H. Forward/backward spatial smoothing techniques for coherent signal identification[J]. IEEE Trans. on Acoustics, Speech, and Signal Processing, 1989, 37(1): 8-15.
[6] Du W and Kirlin R L. Improved spatial smoothing techniques for DOA estimation of coherent signals[J]. IEEE Trans. on Signal Processing, 1991, 39(5): 1208-1210.
[7] Li Jian. Improved angular resolution for spatial smoothing techniques[J]. IEEE Trans. on Signal Processing, 1992, 40(12): 3078-3081.
[8]董玫,张守宏,吴向东.一种改进的空间平滑算法[J].电子与信息学报, 2008, 30(4): 859-862.
[9] Rao B D and Hari K V S. Weighted subspace methods and spatial smoothing analysis and comparison[J]. IEEE Trans. on Signal Processing, 1993, 41(2): 788-803.
[10]王布宏,王永良,陈辉.相干信源波达方向估计的加权空间平滑算法[J].通信学报, 2003, 24(4): 31-40.
[11] Prasad S, Williams R Y, Mahalanabis A K, et al. A transform-based covariance differencing approach for some classes of parameter estimation problems[J]. IEEE Trans. on Acoustics, Speech, and Signal Processing, 1988, 36(5): 631-641.
[12]叶中付,沈凤麟.基于空间差分技术的测向方法[J].电子学报, 24(7): 38-43.
[13] Choi Y-H. Subspace-based coherent sources localization with forward/backward covariance matrices[J]. IEE Proc.-Radar, Sonar and Navigation 2002, 149(3): 145-151.
[14] Qi Chongying, Wang Yongliang, Zhang Yongshun, et al. Spatial difference smoothing for DOA estimation of coherent signals[J]. IEEE Signal Processing Letters, 2005, 12(11): 800-802.
[15] Lee H B and Wengrovitz M S. Resolution threshold of beamspace MUSIC for two closely spaced emitters[J]. IEEE Trans. on Acoustics. Speech. and SignalProcessing, 1990, 38(9): 1545-1559.
[16] Kautz G M and Zoltowski M D. Performance analysis of MUSIC employing conjugate symmetric beamformers[].IEEE Trans. on Signal Processing, 1995, 43(3): 737-748.
[17] Zoltowski M D, Kautz G M, and Silverstein S D. Beamspace Root-MUSIC[J]. IEEE Trans. on Signal Processing, 1993, 41(1): 344-364.
[18] Zoltowski M D, Silverstein S D, and Mathews C P. Beamspace root-MUSIC for minimum redundancy linear arrays[J]. IEEE Trans. on Signal Processing, 1993, 41(7): 2502-2507.
[19] Zoltowski M D, Krogmeier J V, and Kautz G M. Novel multirate processing of beamspace noise eigenvectors[J]. IEEE Signal Processing Letters, 1994, 1(5): 83-86.
[20] Kautz G M and Zoltowski M D. Beamspace DOA estimation featuring multirate eigenvectors processing[J]. IEEE Trans. on Signal Processing, 1996, 44(7): 1765-1778.
[21] Hamza R and Buckley K. Multiple cluster beamspace and resolution-enhanced ESPRIT[J]. IEEE Trans. on Antennas and Propagation, 1994, 42(8): 1041-1052.
[22] Zoltowski M D and Lee T-S. Beamspace ML bearing estimation incorporating low-angle geometry[J]. IEEE Trans. on Aerospace and Electronic Systems, 1991, 27(3): 441-458.
[23] Zoltowski M D and Lee T-S. Maximum likelihood based sensor array signal processing in the beamspace domain for low angle radar tracking[J]. IEEE Trans. on Signal Processing, 1991, 39(3): 656-671.
[24] Mathews C P and Zoltowski M D. Eigenstructure techniques for 2-D angle estimation with uniform circular arrays[J]. IEEE Trans. on Signal Processing, 1994, 42(9): 2395-2407.
[25] Belloni F and Koivunen V. Beamspace transform for UCA: error analysis and bias reduction[J]. IEEE Trans. on Signal Processing, 2006, 54(8): 3078-3089.
[26] Zoltowski M D, Haardt M, and Mathews C P. Closed-form 2-D angle estimation with rectangular arrays in element space or beamspace via unitary ESPRIT[J]. IEEE Trans. on Signal Processing, 1996, 44(2): 316-328.
[27] Mathews C P, Haardt M, and Zoltowski M D. Performance analysis of closed-form, ESPRIT based 2-D angle estimator for rectangular arrays[J]. IEEE Signal Processing Letters, 1996, 3(4): 124-126.
[28] Gansman J A, Zoltowski M D, and Krogmeier J V. Multidimensional multirate DOA estimation in beamspace[J]. IEEE Trans. on Signal Processing, 1996, 44(11): 2780-2792.
[29] Yang Y, Wan C, Sun C, et al. DOA estimation for coherent sources in beamspace using spatial smoothing[C]. Information, Communications and Signal Processing 2003 and the Fouth Pacific Rim Conference on Muitimedia, 2003(2):1028-1032.
[30] Li Hongsheng, He You, Yang Rijie, et al. Beamspace based DOA estimation methods of coherent sources in the presence of impulsive noise[C]. The 8th International Conference on Signal Processing, 2006(4): 16-20.
[31] Phaisal-atsawasenee N and Suleesathira R. Improved angular resolution of beamspace MUSIC for finding direction of coherent sources[C]. The 1st International Symposium on System and Control in Aerospace and Astronautics. 2006: 51-56.
[1] Krim H and Viberg M. Two decades of array signal processing research[J]. IEEE Signal Processing Magazine, 1996, 13(4): 67-94.
[2] Bobin J, Starck J-L, Fadili J, et al. Sparsity and morphological diversity in blind source separation[J]. IEEE Trans. on Image Processing, 2007, 16(11): 2662-2674.
[3] Bobin J, Moudden Y, Starck J-L, et al. Morphological diversity and source separation[J]. IEEE Signal Processing Letters, 2006, 13(7): 409-412.
[4] Li Yuanqing, Amari S, Cichocki A, et al. Underdetermined blind source separation based on sparse representation[J]. IEEE Trans. on Signal Processing, 2006, 54(2): 423-437.
[5] Cheng P, Jiang Y, and Xu R. ISAR imaging based on sparse signal representation with multiple measurement vectors[C]. in Proc. Int. Conf. Radar, Shanghai, China, 2006: 1-4.
[6] Cheng P, Jiang Y, and Xu R. A novel ISAR imaging algorithm for maneuvering targets based on sparse signal representation[C]. The sixth world congress on intelligent control and automation, 2006, 2: 10126-10129.
[7] Sanz J and Huang T. Discrete and continuous band-limited signal extrapolation[J]. IEEE Trans. on Acoustics, Speech, and Signal Processing, 1983, 31(5): 1276-1285.
[8] Lee H, Sullivan D P, and Huang T H. Improvement of discrete bandlimited signal extrapolation by iterative subspace modification. In: Proceedings of International Conference on Acoust, Speech, and Signal Processing, 1987, p. 1569-1572.
[9] Jain A K. Extrapolation algorithm for discrete signals with application in spectral estimation[J]. IEEE Trans. on Acoustics, Speech, and Signal Processing, 1981, 29(4): 830-845.
[10] Cabrera S D and Parks T W. Extrapolation and spectral estimation with iterative weighted norm modification[J]. IEEE Trans. on Acoustics, Speech, and Signal Processing, 1991, 39(4): 842-851.
[11] Gorodnitsky I F and Rao B D. Sparse signal reconstruction from limited data using FOCUSS: a re-weighted minimum norm algorithm[J]. IEEE Trans. on Signal Processing, 1997, 45(3): 600-616.
[12] Liu Hesheng, Schimpf P H, Dong Guoya, et al. Standardized shrinkingLORETA-FOCUSS(SSLOFO): a new algorithm for spatio-temporal EEG source reconstruction[J]. IEEE Trans. on Signal Processing, 2005, 52(10): 1681-1692.
[13] Wipf D P and Rao B D. Bayesian learning for sparse signal reconstruction[C]. IEEE ICASSP, 2003, 6: 601-604.
[14] Wright J, Yang A Y, Ganesh A, et al. Robust face recognition via sparse representation[J]. IEEE Trans. on Pattern Analysis and Machine Intelligence, 2009, 31(2): 210-227.
[15] Chen S and Donoho D. Basis pursuit[C]. in Proc. Twenty-Eighth Asilomar Conf. Signals, Syst., Comput., vol. I, Monterey, CA, Nov. 1994, pp. 41-44.
[16] Romberg J K. Sparse signal recovery via l1 minimization[C]. 2006 40th Annual Conference on Information Sciences and Systems, 2006: 213-215.
[17] Rao B D and Kreutz-Delgado K. An affine scaling methodology for best basis selection[J]. IEEE Trans. on Signal Processing, 1999, 47(1): 187-200.
[18] He Zhaoshui, Cichocki A, Zdunek R, et al. Improved FOCUSS method with conjugate gradient iterations[J]. IEEE Trans. on Signal Processing, 2009, 57(1): 399-404.
[19] Rao B D, Engan K, Cotter S F, et al. Subset selection in noise based on diversity measure minimization. IEEE Trans. on Signal Processing, 2003, 51(3): 760-770.
[20] Jooman Han and Kwang Suk Park. Regularized FOCUSS algorithm for EEG/MEG source imaging[C]. 26th Annual International Conferencr of the Engineering in Medicine and Biology Society, 2004: 122-124.
[21] Xia T Q, Zheng Y, Wan Q, et al. Adaptive regularized FOCUSS algorithm[C]. Information, 2007 IEEE Radar Conference, 2007:282-284.
[22] Cotter S F, Rao B D, Engan K, et al. Sparse solutions to linear inverse problems with multiple measurement vectors[J]. IEEE Trans. on Signal Processing, 2005, 53(7): 2477-2488.
[23] Zdunek R and Cichocki A. Improved M-FOCUSS algorithm with overlapping blocks for locally smooth sparse signals[J]. IEEE Trans. on Signal Processing, 2008, 56(10): 4752-4761.
[24] Dong Liang, Ying L, and Feng Liang. Parallel MRI acceleration using M-FOCUSS[C]. 2009 3rd International Conference on Bioinformatics and Biomedical Engineering, 2009: 1-4.
[25] Malioutov D, Cetin M, and Willsky A S. A sparse signal reconstructionperspective for source localization with sensor arrays. IEEE Trans. on Signal Processing, 2005, 53(8): 3010-3022.
[26] Sacchi M D, Ulrych T, and Walker C J. Interpolation and extrapolation using a high-resolution discrete Fourier transform[J]. IEEE Trans. on Signal Processing, 1998, 46(1): 31-38.
[27] Williams R T, Prasad S, Mahalanbis S K, et al. An improved spatial smoothing technique for bearing estimation in a multipath environment[J]. IEEE Trans. on Acoustics, Speech, and Signal Processing, 1988, 36(4): 425-432.
[28] Pillai S U and Kwon B H. Forward/backward spatial smoothing techniques for coherent signal identification[J]. IEEE Trans. on Acoustics, Speech, and Signal Processing, 1989, 37(1): 8-15.
[29] Chen P, Wu T J, and Yang J. A comparative study of model selection criteria for the number of signals[J]. IET Radar, Sonar & Navigation. 2008, 2(3): 180-188.
[30] Huang Lei, Wu Shun-jun, and Li Xia. Reduced-rank MDL method for source enumeration in high-resolution array processing[J]. IEEE Trans. on Signal Processing, 2007, 55(12): 5658-5667.
[31] Huang Lei, Long Teng, and Wu Shun-jun. Source enumeration for high-resolution array processing using improved gerschgorin radii without eigendecomposition[J]. IEEE Trans. on Signal Processing, 2008, 56(12): 5916-5925.
[32] Zoltowski M D, Haardt M, and Mathews C P. Closed-form 2-D angle estimation with rectangular arrays in element space or beamspace via Unitary ESPRIT[J]. IEEE Trans. on Signal Processing, 1996, 44(2): 316-328.
[33] Zhang Y, Ye Z, Xu X, et al. Estimation of two-dimensional direction-of-arrival for uncorrelated and coherent signals with low complexity[J]. IET Radar Sonar Navig. 2010, 4(4): 507-519.
[1] Zoltowski M D and Lee T-S. Beamspace ML bearing estimation incorporating low-angle geometry[J]. IEEE Trans. on Aerospace Elect Syst, 1991, 27(3): 441-458.
[2] Stoica P, Ottersten B, Viberg M, et al. Maximum likelihood array processing for stochastic coherent sources[J]. IEEE Trans. on Signal Processing, 1996, 44(1): 96-105.
[3] Forster P, Larzabal P, and Boyer E. Threshold performance analysis of maximum likelihood DOA estimation[J]. IEEE Trans. on Signal Processing, 2004, 52(11): 3183-3191.
[4] Tadaion A A, Derakhtian M, Gazor S, et al. A fast multiple-signal detection and localization array signal processing algorithm using the spatial filtering and ML approach[J]. IEEE Trans. on Signal Processing, 2007, 55(5) : 1815-1827.
[5] Shan T J, Wax M, and Kailath T. Adaptive beamforming for coherent signals and interference[J]. IEEE Trans. on Acoustics, Speech, and Signal Processing, 1985, 33(3): 527-536.
[6] Pillai S U and Kwon B H. Forward/backward spatial smoothing techniques for coherent signal identification[J]. IEEE Trans. on Acoustics, Speech, and Signal Processing, 1989, 37(1): 8-15.
[7] Du W and Kirlin R L. Improved spatial smoothing techniques for DOA estimation of coherent signals[J]. IEEE Trans. on Signal Processing, 1991, 39(5): 1208-1210.
[8] Haardt M and Nossek J A. Unitary ESPEIR: how to obtain increased estimation accuracy with a reduced computational burden[J]. IEEE Trans. on Signal Processing, 1995, 43(5): 1232-1242.
[9] Gershman A B and Haardt M. Improving the performance of Unitary ESPRIT via pseudo-noise resampling[J]. IEEE Trans. on Signal Processing, 1999, 47(8): 2305-2308.
[10] Kwakkernaat M R J A E, Jong Y L C, Bultitude R J C, et al. Improved structured least squares for the application of Unitary ESPEIR to cross arrays[J]. IEEE Signal Processing Letters, 2006, 13(6): 349-352.
[11] Haardt M and Gershman A B. A new Unitary-ESPRIT based technique for direction finding[C]. IEEE International Conference on ASSP, 1999, 5: 2837-2840.
[12] Haardt M, Zoltowski M D, Mathews C P, et al. 2D Unitary ESPRIT for efficient 2D parameter estimation[C]. In: Proc IEEE Int Conf Acoustics, Speech, and Signal Processing, Detroit, USA, 1995. 2096-2099.
[13] Zoltowski M D, Haardt M, and Mathews C P. Closed-form 2D angle estimation with rectangular array in element space or beamspace via Unitary ESPRIT[J]. IEEE Trans. on Signal Processing, 1996, 44(2): 316-328.
[14] Ottersten B, Viberg M, and Kailath T. Performance analysis of the total least squares[J]. IEEE Trans. on Signal Processing, 1991, 39(5): 1122-1135.
[15] Viberg M and Ottersten B. Sensor array processing based on subspace fittering[J]. IEEE Trans. on Signal Processing, 1991, 39(5): 1110-1121.
[16] Viberg M, Ottersten B, and Nehorai A. Performance analysis of direction finding with large arrays and finite data[J]. IEEE Trans. on Signal Processing, 1995, 43(2): 469-477.
[17] Swindlehurst A E, Ottersten B, Roy R, et al. Multiple invariance ESPRIT[J]. IEEE Trans. on Signal Processing, 1992, 40(4): 867-881.
[18] Wong K T and Zoltowski M D. Direction-finding with sparse rectangular dual-size spatial invariance array[J]. IEEE Trans. on Aerospace Elect Syst, 1998, 34(4): 1320-1335.
[19] Wong K T and Zoltowski M D. Sparse array aperture extension with dual-size spatial invariances for ESPRIT-based direction finding[C]. In: Proc IEEE 39th Midwest symposium on Circuits and Systems, Ames, USA, 1996. 691-694.
[20] Vasylyshyn V I and Garkusha O A. Closed-form DOA estimation with multiscale Unitary ESPRIT algorithm[C]. In: Proc European Radar Conference, Amsterdam, Netherlands, 2004. 2096-2099.
[21] Vasylyshyn V I. Unitary ESPRIT-based DOA estimation using sparse linear dual size spatial invariance array[C]. In: Proc European Radar Conference, Amsterdam, Netherlands, 2005. 157-160.
[22] Lemma A N, van der Veen A-J, and Deprettere E F. Multiresolution ESPRIT algorithm[J]. IEEE Trans. on Signal Processing, 1999, 47(6): 1722-1726.
[1]吴向东.米波雷达低仰角测高算法及相关技术研究[D].西安:西安电子科技大学博士学位论文, 2008.
[2] Yoganandam Y, Reddy V U, and Uma C. Modified linear prediction method for directions of arrival estimation of narrow-band plane waves[J]. IEEE Trans. on AP, 1989, 37(4): 480-488.
[3] Lo T, Leung H, and Litva J. Low-angle radar tracking in a naval environment using a forward-backward nonlinear prediction method[J]. IEEE Trans. on Oceanic Engineering, 1994, 19(3): 468-475.
[4] Xin Jingmin and Sano A. Linear prediction approach to direction estimation of cyclostationary signals in multipath environment[J]. IEEE Trans. on Signal Processing, 2001, 49(4): 710-720.
[5] Grosicki E, Abed-Meraim K, and Hua Y. A weighted linear prediction method for near-field source localization[J]. IEEE Trans. on Signal Processing, 2005, 53(10): 3651-3660.
[6] Campbell W and Swingler D N. Frequency estimation performance of several weighted Burg algorithms[J]. IEEE Trans. on Signal Processing, 1993, 41(3): 1237-1247.
[7] Capon J. High-resolution frequency-wavenumber spectrum analysis[J]. Proc. of IEEE, 1969, 57(8): 1408-1418.
[8] Rahamim D, Tabrikian J, and Shavit R. Source localization using vector sensor array in a multipath environment[J]. IEEE Trans. on Signal Processing, 2004, 52(11): 3096-3103.
[9] Rubsamen M and Gershman A B. Direction-of-arrival estimation for nonuniform sensor arrars: from manifold separation to Fourier domain MUSIC methods[J]. IEEE Trans. on Signal Processing, 2009, 57(2): 588-599.
[10] Pillai S U and Kwon B H. Forward/backward spatial smoothing techniques for coherent signal identification[J]. IEEE Trans. on Acoustics, Speech, and Signal Processing, 1989, 37(1): 8-15.
[11] Li Jian. Improved angular resolution for spatial smoothing techniques[J]. IEEE Trans. on Signal Processing, 1992, 40(12): 3078-3081.
[12] Selva J. Computation of spectral and root MUSIC through real polynomial rooting[J]. IEEE Trans. on Signal Processing, 2005, 53(5): 1923-1927.
[13] Lee T S. Fast implementation of root-form eigen-based methods for detecting closely spaced sources[J]. IEE Proceedings-F, 1992, 139(4): 288-296.
[14] Gao Feifei and Gershman A B. A generalized ESPRIT approach to direction-of-arrival estimation[J]. IEEE Signal Processing Letters, 2005, 12(3): 254-257.
[15] Haardt M and Nossek J A. Unitary ESPEIR: how to obtain increased estimation accuracy with a reduced computational burden[J]. IEEE Trans. on Signal Processing, 1995, 43(5): 1232-1242.
[16] Gershman A B and Haardt M. Improving the performance of Unitary ESPRIT via pseudo-noise resampling[J]. IEEE Trans. on Signal Processing, 1999, 47(8): 2305-2308.
[17] Stoica P, Ottersten B, and Viberg M, et al. Maximum likelihood array processing for stochastic coherent sources[J]. IEEE Trans. on Signal Processing, 1996, 44(1): 96-105.
[18]贾永康,保铮.时-空二维信号模型下的波达方向估计方法及其性能分析[J].电子学报, 1997, 25(9): 69-73.
[19] Viberg M, Ottersten B, and Kailath T. Detection and estimation in sensor arrays using weighted subspace fitting[J]. IEEE Trans. on Signal Processing, 1991, 39(11): 2436-2449.
[20] Ziskind I and Wax M. Maximum likelihood localization of multiple sources byalternating projection[J]. IEEE Trans. on Acoustics, Speech, and Signal Processing, 1988, 36(10): 1553-1559.
[21]赵光辉,陈伯孝,董玫.基于交替投影的DOA估计方法及其在米波雷达中的应用[J].电子与信息学报, 2008, 30(1): 224-227.
[22] Lo T and Litva J. Low-angle tracking using a multifrequency sampled aperture radar[J]. IEEE Trans. on AES, 1991, 27(5): 797-805.
[23] Lo T and Litva J. Use of a highly deterministic multipath signal model in low-angle tracking[J]. IEE Proceedings-F, 1991, 138(2): 163-171.
[24] Lo T, Wong T, and Litva J. New technique for low-angle radar tracking[J]. Electronics Letters, 1991, 27(6): 529-531.
[25] Bosse E, Turner R M, and Brookes D. Improved radar tracking using a multipath model: maximum likelihood compared with eigenvector analysis[J]. IEE Proc.-Radar, Sonar, Navig. 1994, 141(4): 213-222.
[26] Mahafza B R. Radar systems analysis and design using MATLAB[M]. Chapman & Hall/CRC, 2000.
[27] Long M W.陆地和海面的雷达波散射特性[M].北京:科学出版社, 1981.
[28] Freehafer I E, Fishback W T, Furry W H, et al. Theory of propagation in a horizontal stratified atmosphere. in Kerr D. K. Propagation of short radio waves[M]. McCraw, 1952.
[2] White W D. Low-angle radar tracking in the presence of multipath[J]. IEEE Trans. on Aerospace and Electronic Systems, 1974, 10(6): 835-852.
[3] Giuli D and Tiberio R. A midified monopulse technique for radar tracking with low-angle multipath[J]. IEEE Trans. on Aerospace and Electronic Systems, 1975, 11(5): 741-748.
[4] Mrstik A V and Smith P G. Multipath limitations on low-angle radar tracking[J]. IEEE Trans. on Aerospace and Electronic Systems, 1978, 14(1): 85-102.
[5] Reilly J, Litva J, and Bauman P. New angle-of-arrival estimator: comparative evaluation applied to the low-angle tracking problem[J]. Communications, Radar and Signal Processing, IEE Proceedings F, 1988, 135(5): 408-420.
[6] Sword C K, Simaan M, and Kamen E W. Multiple target angle tracking using sensor array outputs[J]. IEEE Trans. on Aerospace and Electronic Systems, 1990, 26(2): 367-373.
[7] Bruder J A and Saffold J A. Multipath effects on low-angle tracking at millimeter-wave frequencies[J]. IEE Proceedings F, 1991, 138(2): 172-184.
[8] Zoltowski M D and Lee T-S. Maximum likelihood based sensor array signal processing in the beamspace domain for low angle radar tracking[J]. IEEE Trans. on Signal Processing, 1991, 39(3): 656-671.
[9] Rahamim D, Tabrikian J, and Shavit R. Source localization using vector sensor array in a multipath environment[J]. IEEE Trans. on Signal Processing, 2004, 52(11): 3096-3103.
[10] Hurtado M and Nehorai A. Performance analysis of passive low-grazing-angle source localization in maritime environments using vector sensors[J]. IEEE Trans on Aerospace and Electronic Systems, 2007, 43(2): 780-789.
[11]李金梁,李永祯,王雪松.米波极化雷达的反隐身研究[J].雷达科学与技术, 2005, 3(6): 321-326.
[12]万山虎,丁建江.提高米波雷达四抗性能的关键技术[J].中国雷达, 1998, 2: 1-4.
[13]董智文,屈晓光.防空制导雷达反隐身性能分析[C].第十届全国雷达学术年会, 2008: 376-379.
[14]陶茀毓.现代米波雷达介绍[J].地面防空武器, 2002, 4: 34-38.
[15]陈长兴,巩林玉,班斐等.米波谐振雷达反隐身技术研究[J].舰船电子对抗, 2009, 32(4): 34-37.
[16]陈知明.机载雷达的隐身波形[J].现代雷达, 2006, 28(9): 24-26.
[17]王湃,夏明耀,周乐柱,董硕.不同方法分析隐身飞机的散射特性比较[J].电波科学学报, 2004, 19: 69-72.
[18]石晓宁.米波雷达目标跟踪之分析[J].现代电子, 2000, 73(4): 8-12.
[19]盛景泰.米波雷达多径测高技术[J].中国电子科学研究院学报, 2008, 3(5): 510-514.
[20]胡坤娇,吴剑旗.米波雷达低仰角测高方法试验研究[J].第十届全国雷达学术年会, 2008: 63-66.
[21]胡晓琴,陈建文,陈辉.神经网络用语米波雷达测高[J].空军雷达学院学报, 2004, 18(3): 16-19.
[22]董玫,张守宏,吴向东.距离高分辨测高技术[J].火控雷达技术, 2006, 35(1): 10-14.
[23] Capon J. High-resolution frequency-wavenumber spectrum analysis[J]. IEEE Proceedings, 1969, 57(8): 1408-1418.
[24] Schmidt R O. Multiple emitter location and signal parameter estimation[J]. IEEE Trans. on Antennas and Propagation, 1986, 34(3): 276-280.
[25] Pillai S U and Kwon B H. Forward/backward spatial smoothing techniques for coherent signal identification[J]. IEEE Trans. on Acoustic, Speech and Signal Processing, 1989, 37(1): 8-15.
[26] Rao B D and Hari K V S. Performance analysis of Root-MUSIC[J]. IEEE Trans. on Acoustic, Speech and Signal Processing, 1989, 37(12): 1939-1949.
[27] Roy R and Kailath T. ESPRIT-a subspace rotation approach to estimation of parameters of cissoids in noise[J]. IEEE Trans. on Acoustic, Speech and Signal Processing, 1986, 34(10): 1340-1342.
[28] Haardt M and Nossek J A. Unitary ESPRIT: how to obtain increased estimation accuracy with a reduced computational burden[J]. IEEE Trans. on Signal Processing, 1995, 43(5): 1332-1242.
[29] Stoica P and Sharman K. C. Maximum likehilood methods for direction-of–arrival estimation[J]. IEEE Trans. on Acoustic, Speech and Signal Processing, 1990, 38(7): 1132-1143.
[30] Ziskind I. and Wax M. Maximum likelihood localization of multiple sources by alternating projection[J]. IEEE Trans. on Acoustic, Speech and Signal Processing, 1988, 36(10): 1553-1559.
[31]赵永波,张守宏.米波雷达测高技术研究[R].西安电子科技大学电子工程研究所. 2003.
[32]立平.俄罗斯国家防空系统开始部署米波反隐身数字相控阵雷达[J].航天电子对抗, 2004, 4: 42.
[33] http://www.rusarm.ru/cataloque/air_def/air_def_40-43.pdf
[34] http://www.zgjunshi.com/world/tanke/lujileida/200908/20090804174149.html
[35]保铮,张庆文.一种新型的米波雷达--综合脉冲与孔径雷达[J].现代雷达, 1995, 17(1): 1-13.
[36]张庆文.综合脉冲与孔径雷达系统性能分析与研究[D].西安:西安电子科技大学博士学位论文, 1994.
[37]陈伯孝. SIAR及其四维跟踪处理等技术研究[D].西安:西安电子科技大学博士学位论文, 1997.
[38] Chen Baixiao, Zhang Shouhong, Wu Jianqi, et al. Analysis and experimental results on Sparse-array Synthetic Impulse and Aperture Radar[C]. Proc. of 2006 CIE international conf. on radar, Beijing, China, 2001:76-80.
[39]吴剑旗,贺瑞龙,江凯等.稀布阵综合脉冲孔径雷达的研究与实验[J].现代电子, 1998, 64(3): 1-5.
[40]杨克虎,保铮.多频应用对低角跟踪方向估计性能的改善[J].西安电子科技大学学报, 1993, 20(4): 181-193.
[41]张平,张晓华.一种低仰角跟踪的新方法[J].现代雷达, 2001, 12(6): 9-12.
[42]何子述,黎敏,荆玉兰.频率捷变的相控阵雷达目标多径DOA估计算法[J].系统工程与电子技术, 2005, 27(11): 1880-1882.
[43] Sherman S M.Complex indicated angles applied to unresolved radar targets and multipath[J]. IEEE Trans. on AES, 1971(1): 160-170.
[44] Peyton P Z and Goldman L. Radar performance with multipath using the complex angle[J]. IEEE Trans. on AES, 1971, 7(1): 171-178.
[45] Howard D D, Sherman S M, and Campbell J J. Experimental results of the complex indicated angle techique for multipath correction[J]. IEEE Trans. on AES, 1974, 10(6): 779-787.
[46] Knepp D L. Variance and bias of angle estimation radars[J]. IEEE Trans. on Antennas and Propagation, 1976, 24(4): 518-521.
[47] Zheng Y B, Tseng S M, and Yu K B. Closed-form four-channel monopulse two-target resolution[J]. IEEE Trans. on AES, 2003, 39(3): 1083-1089.
[48] Nickel U and Fhr F. Overview of generalized monopulse estimation[J]. IEEE A&E Systems Magazine, 2006, 21(6): 27-56.
[49]范志杰,尚社.一种用于低空目标探测与跟踪的新方法[J].雷达科学与技术, 2004, 2(3): 153-157.
[50]贾永康,保铮.利用多普勒信息的波达方向最大似然估计方法[J].电子学报, 1997, 25(6): 71-76.
[51]贾永康,保铮.时-空二维信号模型下的波达方向估计方法及其性能分析[J].电子学报, 1997, 25(9): 69-73.
[52]赵光辉,陈伯孝,董玫.基于交替投影的DOA估计方法及其在米波雷达中的应用[J].电子与信息学报, 2008, 30(1): 224-227.
[53]胡铁军,杨雪亚,陈伯孝.阵列内插的波束域ML米波雷达测高方法[J].电波科学学报, 2009, 24(4): 660-666.
[54]吴向东,张守宏,董玫.一种基于线性预处理的米波雷达低仰角处理算法[J].电子学报, 2006, 34(9): 1668-1671.
[55]张文俊,赵永波,张守宏.广义MUSIC算法在米波雷达测高中的应用及其改进[J].电子与信息学报, 2007, 29(2): 387-390.
[56]刘俊,刘峥,刘韵佛.米波雷达仰角和多径衰减系数联合估计算法[J].电子与信息学报, 2011, 33(1): 33-37.
[57]陈伯孝,胡铁军,郑自良等.基于波瓣分裂的米波雷达低仰角测高方法及其应用[J].电子学报, 2007, 35(6): 1021-1025.
[58] Baixiao Chen, Guanghui Zhao and Shouhong Zhang. Altitude measurement based on beam split and frequency diversity in VHF radar[J]. IEEE Trans. on Aerospace and Electronic Systems, 2010, 46(1): 3-13.
[59] Lo T and Litva J. Low-angle tracking using a multifrequency sampled aperture radar[J]. IEEE Trans. on AES, 1991, 27(5): 797-805.
[60] Lo T and Litva J. Use of a highly deterministic multipath signal model in low-angle tracking[J]. IEE Proceedings-F, 1991, 138(2): 163-171.
[61] Lo T, Wong T, and Litva J. New technique for low-angle radar tracking[J]. Electronics Letters, 1991, 27(6): 529-531.
[1] Schmidt R O. Multiple emitter location and signal parameter estimation[J]. IEEE Trans. on Antennas and Propagation, 1986, 34(3): 276-280.
[2] Rao B D and Hari K V S. Performance analysis of Root-MUSIC[J]. IEEE Trans. on Acoustic, Speech and Signal Processing, 1989, 37(12): 1939-1949.
[3] Zlotowski M D, Kautz G M, and Silverstein S D. Beamspace root-MUSIC[J].IEEE Trans. on Signal Processing, 1993, 41(1): 344-364.
[4] Thompson J S, Grant P M, and Mulgrew B. Performance of spatial smoothing algorithms for correlated sources[J]. IEEE Trans. on Signal Processing, 1996, 44(4): 1040-1046.
[5] Al-Ardi E M, Shubair R M, and Al-Mualla M E. Computationally efficient high-resolution DOA estimation in multipath environment[J]. Electronices Letters, 2004, 40(14): 908-910.
[6] Lin J D, Fang W H, Wang Y Y, et al. FSF MUSIC for joint DOA and frequency estimation and its performance analysis[J]. IEEE Trans. on Signal Processing, 2006, 54(12): 4529-4542.
[7] Ying Zhang and Boon Poh Ng. MUSIC-like DOA estimation without estimating the number of sources[J]. IEEE Trans. on Signal Processing, 2010, 58(3): 1668-1676.
[8] Roy R and and Kailath T. ESPRIT- a subspace rotation approach to estimation of parameters of cissoids in noise[J]. IEEE Trans. on Acoustic, Speech and Signal Processing, 1986, 34(10): 1340-1342.
[9] Ottersten B, Viberg M, and Kailath T. Performance analysis of the total least squares ESPRIT algorithm[J]. IEEE Trans. on Signal Processing, 1991, 39(5): 1122-1135.
[10] Haardt M and Nossek J A. Unitary ESPRIT: how to obtain increased estimation accuracy with a reduced computational burden[J]. IEEE Trans. on Signal Processing, 1995, 43(5): 1232-1242.
[11] Xu Guanghan, Silverstein S D, Roy R H., et al. Beamspace ESPRIT[J]. IEEE Trans. on Signal Processing, 1994, 42(2): 349-356.
[12] Lemma A N, Veen A J, and Deprettere E F. Multiresolution ESPRIT algorithm[J]. IEEE Trans. on Signal Processing, 1999, 47(6): 1722-1726.
[13] Gao Feifei and Gershman A B. A generalized ESPRIT approach to direction-of-arrival estimation[J]. IEEE Signal Processing Letters. 2005, 12(3): 254-257.
[14] Owsley N L. Adaptive data orthogonalization[C]. 1978 International Conference on Acoustics, Speech, and Signal Processing. Tulsa: IEEE, 1978: 109-112.
[15] Thompson P A. An adaptive spectral analysis technique for unbiased frequency estimation in the presence of white noise[C]. Proc.13th Asilomar Conf. Circuits, Syst. Cmput., Pacific Grove, CA: CSA, 1980: 529-533.
[16] Durrani T S, and Sharman K C. Eigenfilter approaches to adaptive array processing[J]. IEE Proc. H on Microwaves, Optics and Antennas, 1983, 130(1): 22-28.
[17] Fuhrmann D R and Liu B. Rotational search methods for adaptive pisarenko harmonic retrieval[J]. IEEE Trans. on Acoustics, Speech, and Signal Processing, 1986, 34(6): 1550-1565.
[18] Yang J-F, and Kaveh M. Adaptive eigensubspace algorithms for direction or frequency estimation and tracking[J]. IEEE Trans. on Acoustics, Speech, and Signal Processing, 1988, 36(2): 241-251.
[19] Babu K V S. A fast algorithm for adaptive estimation of root-MUSIC polynomial coefficients[C]. 1991 International Conference on Acoustics, Speech, and Signal Processing. Toronto, Ont.: IEEE, 1991: 2229-2232.
[20] Pesavento M, Gershman A B, and Haardt M. A theoretical and experimental performance study of a root-MUSIC algorithm based on a real-valued eigendecomposition[C]. 2000 International Conference on Istanbul: IEEE, 2000: 3049-3052. Acoustics, Speech, and Signal Processing.
[21] Pesavento M, Gershman A B, and Haardt M. Unitary root-MUSIC with a real-valued eigendecomposition: a theoretical and experimental performance study[J]. IEEE Trans. on Signal Processing, 2000, 48(5): 1306-1314.
[22] Ferreira T N, Netto S L, and Diniz P S R. Convariance-based direction-of-arrival estimation with real structures[J]. IEEE Signal Processing Letters. 2008, 15: 757-760.
[23] Gershman A B and Haardt M. Improving the performance of Unitary ESPRIT via pseudo-noise resampling[J]. IEEE Trans. on Signal Processing, 1999, 47(8): 2305-2308.
[24] Ottersten B, Viberg M, and Kailath T. Performance analysis of the total least squares ESPRIT algorithm[J]. IEEE Trans. on Signal Processing, 1991, 39(5): 1122-1135.
[25] Bachl R. The forward-backward averaging technique applied to TLS-ESPRIT algorithm[J]. IEEE Trans. on Signal Processing, 1991, 43(11): 2691-2699.
[26]杨雪亚,陈伯孝,朱根生.基于实值特征子空间迭代的DOA估计算法[J].西安电子科技大学学报, 2010, 37(2): 242-247.
[27] Zoltowski M D, Haardt M, and Mathews C P. Closed-form 2-D angle estimation with rectangular arrays in element space or beamspace via Unitary ESPRIT[J]. IEEE Trans. on Signal Processing, 1996, 44(2): 316-328.
[28] Zhang Y, Ye Z., Xu X, et al. Estimation of two-dimensional direction-of-arrival for uncorrelated and coherent signals with low complexity[J]. IET Radar Sonar Navig. 2010, 4(4): 507-519.
[1] Pesavento M, Gershman A B, and Haardt M. A theoretical and experimental performance study of a root-MUSIC algorithm based on a real-valued eigendecomposition[C]. 2000 International Conference on Acoustics, Speech, and Signal Processing. Istanbul: IEEE, 2000: 3049-3052.
[2] Pesavento M, Gershman A B, and Haardt M. Unitary root-MUSIC with a real-valued eigendecomposition: a theoretical and experimental performance study[J]. IEEE Trans. on Signal Processing, 2000, 48(5): 1306-1314.
[3] Ferreira T N, Netto S L, and Diniz P S R. Convariance-based direction-of-arrival estimation with real structures[J]. IEEE Signal Processing Letters. 2008, 15: 757-760.
[4] Li J and Stoica P. Efficient mixed-spectrum estimation with applications to target feature extraction[J]. IEEE Trans. on Signal Processing, 1996, 44(2): 281-295.
[5] Li J and Stoica P. Efficient mixed-spectrum estimation with applications to target feature extraction[J]. IEEE Proceedings of ASILOMAR, 1995, 1: 428-432.
[6] Li J, Stoica P, and Zheng Dunmin. Angle and waveform estimation in the presence of colored noise via RELAX[J]. IEEE Proceedings of ASILOMAR, 1995, 1: 433-437.
[7] Jian C, Wang S, and Lin L. Two-dimensional DOA estimation of coherent signals based on 2D Unitary ESPRIT method[C]. The 8th International Conference on Signal Processing. China: Beijing, 2006: 16-20.
[8] Zoltowski M D, Haardt M, and Mathews C P. Closed-form 2-D angle estimation with rectangular arrays in element space or beamspace via Unitary ESPEIT[J]. IEEE Trans. on Signal Processing, 1996, 44(2): 316-328.
[9] Zhang Y, Ye Z, Xu X, et al. Estimation of two-dimensional direction-of-arrival for uncorrelated and coherent signals with low complexity[J]. IET Radar Sonar Navig. 2010, 4(4): 507-519.
[10] Li Jian, Zheng Dunmin, and Stoica P. Angle and waveform estimation via RELAX[J]. IEEE Trans. on Aerospace and Electronic Systems, 1997, 33(3): 1077-1087.
[11] Liu Z-S and Li J. Implementation of the RELAX algorithm[J]. IEEE Transactions on Aerospace and Electronic Systems, 1998, 34(2): 657-664.
[12]邵朝,保铮. RELAX算法的相关域分析[J].西安电子科技大学学报, 1997, 24(2): 164-171.
[13]邵朝,保铮. RELAX算法的分辨特性分析[J].电子科学学刊, 1999, 21(4): 447-454.
[14] Ye X D, Fang D G, Sheng W X, et al. 2-D angle estimation with a uniform circular array via RELAX[C]. International Conference on Computational Electromagnetics and Its Applications, 1999: 175-178.
[15]叶晓东,李尚生,曹东.基于RELAX算法的三维立体线阵方位角俯仰角联合估计[C].第九届全国雷达学术年会, 2004.
[16]苏晋,陈付彬,张军等.基于RELAX谱估计的波束内多运动目标分辨[J].系统工程与电子技术, 2003, 25(11): 1313-1317.
[17]黄晓涛,梁甸农.基于RELAX的UWB-SAR抑制PFI算法[J].国防科技大学学报, 2000, 22(2): 55-59.
[18] Zheng Yiming, Bao Zheng. Autofousing of SAR images based on RELAX[C]. IEEE International Radar Conference, 2000, 533-538.
[19] Vignaud L. Wavelet-RELAX feature extraction in radar images[J]. IEE Proc.-Radar, Sonar and Navigation, 2003, 150(4): 242-246.
[20] Jiao Yun, Yu Jizhou, and Che Renquan. Application of RELAX algorithm to ISAR superresolution imaging[C]. International Conference on Radar, Shanghai, 2006: 1-4.
[21]张超峰,刘丹,程臻.采用RELAX算法提高单脉冲三维成像横向分辨率[J].系统工程与电子技术, 2008, 30(11): 2063-2065.
[22] Zhou Xusheng, Chen Bin, and Sheng Weixing. Application of 2-D RELAX algorithm to SAR superresolution imaging[C]. International Conference on Microwave and Millimeter Wave Technology, 2008: 220-223.
[1] Evans J E, Johnson J R, and Sun D F. High resolution angular spectrum estimation techniques for terrain scattering analysis and angle of arrival estimation[A]. IEEE 1st ASSP workshop spectral estimation, Hamilton, Ontario Canada, 1981: 134-139.
[2] Shan T J, Wax M, and Kailath T. Adaptive beamforming for coherent signals and interference[J]. IEEE Trans. on Acoustics, Speech, and Signal Processing, 1985, 33(3): 527-536.
[3] Shan T J, Wax M, and Kailath T. On spatial smoothing for estimation of coherent signals[J]. IEEE Trans. on Acoustics, Speech, and Signal Processing, 1985, 33(4): 806-811.
[4] Williams R T, Prasad S, Mahalanbis S K, et al. An improved spatial smoothing technique for bearing estimation in a multipath environment[J]. IEEE Trans. on Acoustics, Speech, and Signal Processing, 1988, 36(4): 425-432.
[5] Pillai S U, and Kwon B H. Forward/backward spatial smoothing techniques for coherent signal identification[J]. IEEE Trans. on Acoustics, Speech, and Signal Processing, 1989, 37(1): 8-15.
[6] Du W and Kirlin R L. Improved spatial smoothing techniques for DOA estimation of coherent signals[J]. IEEE Trans. on Signal Processing, 1991, 39(5): 1208-1210.
[7] Li Jian. Improved angular resolution for spatial smoothing techniques[J]. IEEE Trans. on Signal Processing, 1992, 40(12): 3078-3081.
[8]董玫,张守宏,吴向东.一种改进的空间平滑算法[J].电子与信息学报, 2008, 30(4): 859-862.
[9] Rao B D and Hari K V S. Weighted subspace methods and spatial smoothing analysis and comparison[J]. IEEE Trans. on Signal Processing, 1993, 41(2): 788-803.
[10]王布宏,王永良,陈辉.相干信源波达方向估计的加权空间平滑算法[J].通信学报, 2003, 24(4): 31-40.
[11] Prasad S, Williams R Y, Mahalanabis A K, et al. A transform-based covariance differencing approach for some classes of parameter estimation problems[J]. IEEE Trans. on Acoustics, Speech, and Signal Processing, 1988, 36(5): 631-641.
[12]叶中付,沈凤麟.基于空间差分技术的测向方法[J].电子学报, 24(7): 38-43.
[13] Choi Y-H. Subspace-based coherent sources localization with forward/backward covariance matrices[J]. IEE Proc.-Radar, Sonar and Navigation 2002, 149(3): 145-151.
[14] Qi Chongying, Wang Yongliang, Zhang Yongshun, et al. Spatial difference smoothing for DOA estimation of coherent signals[J]. IEEE Signal Processing Letters, 2005, 12(11): 800-802.
[15] Lee H B and Wengrovitz M S. Resolution threshold of beamspace MUSIC for two closely spaced emitters[J]. IEEE Trans. on Acoustics. Speech. and SignalProcessing, 1990, 38(9): 1545-1559.
[16] Kautz G M and Zoltowski M D. Performance analysis of MUSIC employing conjugate symmetric beamformers[].IEEE Trans. on Signal Processing, 1995, 43(3): 737-748.
[17] Zoltowski M D, Kautz G M, and Silverstein S D. Beamspace Root-MUSIC[J]. IEEE Trans. on Signal Processing, 1993, 41(1): 344-364.
[18] Zoltowski M D, Silverstein S D, and Mathews C P. Beamspace root-MUSIC for minimum redundancy linear arrays[J]. IEEE Trans. on Signal Processing, 1993, 41(7): 2502-2507.
[19] Zoltowski M D, Krogmeier J V, and Kautz G M. Novel multirate processing of beamspace noise eigenvectors[J]. IEEE Signal Processing Letters, 1994, 1(5): 83-86.
[20] Kautz G M and Zoltowski M D. Beamspace DOA estimation featuring multirate eigenvectors processing[J]. IEEE Trans. on Signal Processing, 1996, 44(7): 1765-1778.
[21] Hamza R and Buckley K. Multiple cluster beamspace and resolution-enhanced ESPRIT[J]. IEEE Trans. on Antennas and Propagation, 1994, 42(8): 1041-1052.
[22] Zoltowski M D and Lee T-S. Beamspace ML bearing estimation incorporating low-angle geometry[J]. IEEE Trans. on Aerospace and Electronic Systems, 1991, 27(3): 441-458.
[23] Zoltowski M D and Lee T-S. Maximum likelihood based sensor array signal processing in the beamspace domain for low angle radar tracking[J]. IEEE Trans. on Signal Processing, 1991, 39(3): 656-671.
[24] Mathews C P and Zoltowski M D. Eigenstructure techniques for 2-D angle estimation with uniform circular arrays[J]. IEEE Trans. on Signal Processing, 1994, 42(9): 2395-2407.
[25] Belloni F and Koivunen V. Beamspace transform for UCA: error analysis and bias reduction[J]. IEEE Trans. on Signal Processing, 2006, 54(8): 3078-3089.
[26] Zoltowski M D, Haardt M, and Mathews C P. Closed-form 2-D angle estimation with rectangular arrays in element space or beamspace via unitary ESPRIT[J]. IEEE Trans. on Signal Processing, 1996, 44(2): 316-328.
[27] Mathews C P, Haardt M, and Zoltowski M D. Performance analysis of closed-form, ESPRIT based 2-D angle estimator for rectangular arrays[J]. IEEE Signal Processing Letters, 1996, 3(4): 124-126.
[28] Gansman J A, Zoltowski M D, and Krogmeier J V. Multidimensional multirate DOA estimation in beamspace[J]. IEEE Trans. on Signal Processing, 1996, 44(11): 2780-2792.
[29] Yang Y, Wan C, Sun C, et al. DOA estimation for coherent sources in beamspace using spatial smoothing[C]. Information, Communications and Signal Processing 2003 and the Fouth Pacific Rim Conference on Muitimedia, 2003(2):1028-1032.
[30] Li Hongsheng, He You, Yang Rijie, et al. Beamspace based DOA estimation methods of coherent sources in the presence of impulsive noise[C]. The 8th International Conference on Signal Processing, 2006(4): 16-20.
[31] Phaisal-atsawasenee N and Suleesathira R. Improved angular resolution of beamspace MUSIC for finding direction of coherent sources[C]. The 1st International Symposium on System and Control in Aerospace and Astronautics. 2006: 51-56.
[1] Krim H and Viberg M. Two decades of array signal processing research[J]. IEEE Signal Processing Magazine, 1996, 13(4): 67-94.
[2] Bobin J, Starck J-L, Fadili J, et al. Sparsity and morphological diversity in blind source separation[J]. IEEE Trans. on Image Processing, 2007, 16(11): 2662-2674.
[3] Bobin J, Moudden Y, Starck J-L, et al. Morphological diversity and source separation[J]. IEEE Signal Processing Letters, 2006, 13(7): 409-412.
[4] Li Yuanqing, Amari S, Cichocki A, et al. Underdetermined blind source separation based on sparse representation[J]. IEEE Trans. on Signal Processing, 2006, 54(2): 423-437.
[5] Cheng P, Jiang Y, and Xu R. ISAR imaging based on sparse signal representation with multiple measurement vectors[C]. in Proc. Int. Conf. Radar, Shanghai, China, 2006: 1-4.
[6] Cheng P, Jiang Y, and Xu R. A novel ISAR imaging algorithm for maneuvering targets based on sparse signal representation[C]. The sixth world congress on intelligent control and automation, 2006, 2: 10126-10129.
[7] Sanz J and Huang T. Discrete and continuous band-limited signal extrapolation[J]. IEEE Trans. on Acoustics, Speech, and Signal Processing, 1983, 31(5): 1276-1285.
[8] Lee H, Sullivan D P, and Huang T H. Improvement of discrete bandlimited signal extrapolation by iterative subspace modification. In: Proceedings of International Conference on Acoust, Speech, and Signal Processing, 1987, p. 1569-1572.
[9] Jain A K. Extrapolation algorithm for discrete signals with application in spectral estimation[J]. IEEE Trans. on Acoustics, Speech, and Signal Processing, 1981, 29(4): 830-845.
[10] Cabrera S D and Parks T W. Extrapolation and spectral estimation with iterative weighted norm modification[J]. IEEE Trans. on Acoustics, Speech, and Signal Processing, 1991, 39(4): 842-851.
[11] Gorodnitsky I F and Rao B D. Sparse signal reconstruction from limited data using FOCUSS: a re-weighted minimum norm algorithm[J]. IEEE Trans. on Signal Processing, 1997, 45(3): 600-616.
[12] Liu Hesheng, Schimpf P H, Dong Guoya, et al. Standardized shrinkingLORETA-FOCUSS(SSLOFO): a new algorithm for spatio-temporal EEG source reconstruction[J]. IEEE Trans. on Signal Processing, 2005, 52(10): 1681-1692.
[13] Wipf D P and Rao B D. Bayesian learning for sparse signal reconstruction[C]. IEEE ICASSP, 2003, 6: 601-604.
[14] Wright J, Yang A Y, Ganesh A, et al. Robust face recognition via sparse representation[J]. IEEE Trans. on Pattern Analysis and Machine Intelligence, 2009, 31(2): 210-227.
[15] Chen S and Donoho D. Basis pursuit[C]. in Proc. Twenty-Eighth Asilomar Conf. Signals, Syst., Comput., vol. I, Monterey, CA, Nov. 1994, pp. 41-44.
[16] Romberg J K. Sparse signal recovery via l1 minimization[C]. 2006 40th Annual Conference on Information Sciences and Systems, 2006: 213-215.
[17] Rao B D and Kreutz-Delgado K. An affine scaling methodology for best basis selection[J]. IEEE Trans. on Signal Processing, 1999, 47(1): 187-200.
[18] He Zhaoshui, Cichocki A, Zdunek R, et al. Improved FOCUSS method with conjugate gradient iterations[J]. IEEE Trans. on Signal Processing, 2009, 57(1): 399-404.
[19] Rao B D, Engan K, Cotter S F, et al. Subset selection in noise based on diversity measure minimization. IEEE Trans. on Signal Processing, 2003, 51(3): 760-770.
[20] Jooman Han and Kwang Suk Park. Regularized FOCUSS algorithm for EEG/MEG source imaging[C]. 26th Annual International Conferencr of the Engineering in Medicine and Biology Society, 2004: 122-124.
[21] Xia T Q, Zheng Y, Wan Q, et al. Adaptive regularized FOCUSS algorithm[C]. Information, 2007 IEEE Radar Conference, 2007:282-284.
[22] Cotter S F, Rao B D, Engan K, et al. Sparse solutions to linear inverse problems with multiple measurement vectors[J]. IEEE Trans. on Signal Processing, 2005, 53(7): 2477-2488.
[23] Zdunek R and Cichocki A. Improved M-FOCUSS algorithm with overlapping blocks for locally smooth sparse signals[J]. IEEE Trans. on Signal Processing, 2008, 56(10): 4752-4761.
[24] Dong Liang, Ying L, and Feng Liang. Parallel MRI acceleration using M-FOCUSS[C]. 2009 3rd International Conference on Bioinformatics and Biomedical Engineering, 2009: 1-4.
[25] Malioutov D, Cetin M, and Willsky A S. A sparse signal reconstructionperspective for source localization with sensor arrays. IEEE Trans. on Signal Processing, 2005, 53(8): 3010-3022.
[26] Sacchi M D, Ulrych T, and Walker C J. Interpolation and extrapolation using a high-resolution discrete Fourier transform[J]. IEEE Trans. on Signal Processing, 1998, 46(1): 31-38.
[27] Williams R T, Prasad S, Mahalanbis S K, et al. An improved spatial smoothing technique for bearing estimation in a multipath environment[J]. IEEE Trans. on Acoustics, Speech, and Signal Processing, 1988, 36(4): 425-432.
[28] Pillai S U and Kwon B H. Forward/backward spatial smoothing techniques for coherent signal identification[J]. IEEE Trans. on Acoustics, Speech, and Signal Processing, 1989, 37(1): 8-15.
[29] Chen P, Wu T J, and Yang J. A comparative study of model selection criteria for the number of signals[J]. IET Radar, Sonar & Navigation. 2008, 2(3): 180-188.
[30] Huang Lei, Wu Shun-jun, and Li Xia. Reduced-rank MDL method for source enumeration in high-resolution array processing[J]. IEEE Trans. on Signal Processing, 2007, 55(12): 5658-5667.
[31] Huang Lei, Long Teng, and Wu Shun-jun. Source enumeration for high-resolution array processing using improved gerschgorin radii without eigendecomposition[J]. IEEE Trans. on Signal Processing, 2008, 56(12): 5916-5925.
[32] Zoltowski M D, Haardt M, and Mathews C P. Closed-form 2-D angle estimation with rectangular arrays in element space or beamspace via Unitary ESPRIT[J]. IEEE Trans. on Signal Processing, 1996, 44(2): 316-328.
[33] Zhang Y, Ye Z, Xu X, et al. Estimation of two-dimensional direction-of-arrival for uncorrelated and coherent signals with low complexity[J]. IET Radar Sonar Navig. 2010, 4(4): 507-519.
[1] Zoltowski M D and Lee T-S. Beamspace ML bearing estimation incorporating low-angle geometry[J]. IEEE Trans. on Aerospace Elect Syst, 1991, 27(3): 441-458.
[2] Stoica P, Ottersten B, Viberg M, et al. Maximum likelihood array processing for stochastic coherent sources[J]. IEEE Trans. on Signal Processing, 1996, 44(1): 96-105.
[3] Forster P, Larzabal P, and Boyer E. Threshold performance analysis of maximum likelihood DOA estimation[J]. IEEE Trans. on Signal Processing, 2004, 52(11): 3183-3191.
[4] Tadaion A A, Derakhtian M, Gazor S, et al. A fast multiple-signal detection and localization array signal processing algorithm using the spatial filtering and ML approach[J]. IEEE Trans. on Signal Processing, 2007, 55(5) : 1815-1827.
[5] Shan T J, Wax M, and Kailath T. Adaptive beamforming for coherent signals and interference[J]. IEEE Trans. on Acoustics, Speech, and Signal Processing, 1985, 33(3): 527-536.
[6] Pillai S U and Kwon B H. Forward/backward spatial smoothing techniques for coherent signal identification[J]. IEEE Trans. on Acoustics, Speech, and Signal Processing, 1989, 37(1): 8-15.
[7] Du W and Kirlin R L. Improved spatial smoothing techniques for DOA estimation of coherent signals[J]. IEEE Trans. on Signal Processing, 1991, 39(5): 1208-1210.
[8] Haardt M and Nossek J A. Unitary ESPEIR: how to obtain increased estimation accuracy with a reduced computational burden[J]. IEEE Trans. on Signal Processing, 1995, 43(5): 1232-1242.
[9] Gershman A B and Haardt M. Improving the performance of Unitary ESPRIT via pseudo-noise resampling[J]. IEEE Trans. on Signal Processing, 1999, 47(8): 2305-2308.
[10] Kwakkernaat M R J A E, Jong Y L C, Bultitude R J C, et al. Improved structured least squares for the application of Unitary ESPEIR to cross arrays[J]. IEEE Signal Processing Letters, 2006, 13(6): 349-352.
[11] Haardt M and Gershman A B. A new Unitary-ESPRIT based technique for direction finding[C]. IEEE International Conference on ASSP, 1999, 5: 2837-2840.
[12] Haardt M, Zoltowski M D, Mathews C P, et al. 2D Unitary ESPRIT for efficient 2D parameter estimation[C]. In: Proc IEEE Int Conf Acoustics, Speech, and Signal Processing, Detroit, USA, 1995. 2096-2099.
[13] Zoltowski M D, Haardt M, and Mathews C P. Closed-form 2D angle estimation with rectangular array in element space or beamspace via Unitary ESPRIT[J]. IEEE Trans. on Signal Processing, 1996, 44(2): 316-328.
[14] Ottersten B, Viberg M, and Kailath T. Performance analysis of the total least squares[J]. IEEE Trans. on Signal Processing, 1991, 39(5): 1122-1135.
[15] Viberg M and Ottersten B. Sensor array processing based on subspace fittering[J]. IEEE Trans. on Signal Processing, 1991, 39(5): 1110-1121.
[16] Viberg M, Ottersten B, and Nehorai A. Performance analysis of direction finding with large arrays and finite data[J]. IEEE Trans. on Signal Processing, 1995, 43(2): 469-477.
[17] Swindlehurst A E, Ottersten B, Roy R, et al. Multiple invariance ESPRIT[J]. IEEE Trans. on Signal Processing, 1992, 40(4): 867-881.
[18] Wong K T and Zoltowski M D. Direction-finding with sparse rectangular dual-size spatial invariance array[J]. IEEE Trans. on Aerospace Elect Syst, 1998, 34(4): 1320-1335.
[19] Wong K T and Zoltowski M D. Sparse array aperture extension with dual-size spatial invariances for ESPRIT-based direction finding[C]. In: Proc IEEE 39th Midwest symposium on Circuits and Systems, Ames, USA, 1996. 691-694.
[20] Vasylyshyn V I and Garkusha O A. Closed-form DOA estimation with multiscale Unitary ESPRIT algorithm[C]. In: Proc European Radar Conference, Amsterdam, Netherlands, 2004. 2096-2099.
[21] Vasylyshyn V I. Unitary ESPRIT-based DOA estimation using sparse linear dual size spatial invariance array[C]. In: Proc European Radar Conference, Amsterdam, Netherlands, 2005. 157-160.
[22] Lemma A N, van der Veen A-J, and Deprettere E F. Multiresolution ESPRIT algorithm[J]. IEEE Trans. on Signal Processing, 1999, 47(6): 1722-1726.
[1]吴向东.米波雷达低仰角测高算法及相关技术研究[D].西安:西安电子科技大学博士学位论文, 2008.
[2] Yoganandam Y, Reddy V U, and Uma C. Modified linear prediction method for directions of arrival estimation of narrow-band plane waves[J]. IEEE Trans. on AP, 1989, 37(4): 480-488.
[3] Lo T, Leung H, and Litva J. Low-angle radar tracking in a naval environment using a forward-backward nonlinear prediction method[J]. IEEE Trans. on Oceanic Engineering, 1994, 19(3): 468-475.
[4] Xin Jingmin and Sano A. Linear prediction approach to direction estimation of cyclostationary signals in multipath environment[J]. IEEE Trans. on Signal Processing, 2001, 49(4): 710-720.
[5] Grosicki E, Abed-Meraim K, and Hua Y. A weighted linear prediction method for near-field source localization[J]. IEEE Trans. on Signal Processing, 2005, 53(10): 3651-3660.
[6] Campbell W and Swingler D N. Frequency estimation performance of several weighted Burg algorithms[J]. IEEE Trans. on Signal Processing, 1993, 41(3): 1237-1247.
[7] Capon J. High-resolution frequency-wavenumber spectrum analysis[J]. Proc. of IEEE, 1969, 57(8): 1408-1418.
[8] Rahamim D, Tabrikian J, and Shavit R. Source localization using vector sensor array in a multipath environment[J]. IEEE Trans. on Signal Processing, 2004, 52(11): 3096-3103.
[9] Rubsamen M and Gershman A B. Direction-of-arrival estimation for nonuniform sensor arrars: from manifold separation to Fourier domain MUSIC methods[J]. IEEE Trans. on Signal Processing, 2009, 57(2): 588-599.
[10] Pillai S U and Kwon B H. Forward/backward spatial smoothing techniques for coherent signal identification[J]. IEEE Trans. on Acoustics, Speech, and Signal Processing, 1989, 37(1): 8-15.
[11] Li Jian. Improved angular resolution for spatial smoothing techniques[J]. IEEE Trans. on Signal Processing, 1992, 40(12): 3078-3081.
[12] Selva J. Computation of spectral and root MUSIC through real polynomial rooting[J]. IEEE Trans. on Signal Processing, 2005, 53(5): 1923-1927.
[13] Lee T S. Fast implementation of root-form eigen-based methods for detecting closely spaced sources[J]. IEE Proceedings-F, 1992, 139(4): 288-296.
[14] Gao Feifei and Gershman A B. A generalized ESPRIT approach to direction-of-arrival estimation[J]. IEEE Signal Processing Letters, 2005, 12(3): 254-257.
[15] Haardt M and Nossek J A. Unitary ESPEIR: how to obtain increased estimation accuracy with a reduced computational burden[J]. IEEE Trans. on Signal Processing, 1995, 43(5): 1232-1242.
[16] Gershman A B and Haardt M. Improving the performance of Unitary ESPRIT via pseudo-noise resampling[J]. IEEE Trans. on Signal Processing, 1999, 47(8): 2305-2308.
[17] Stoica P, Ottersten B, and Viberg M, et al. Maximum likelihood array processing for stochastic coherent sources[J]. IEEE Trans. on Signal Processing, 1996, 44(1): 96-105.
[18]贾永康,保铮.时-空二维信号模型下的波达方向估计方法及其性能分析[J].电子学报, 1997, 25(9): 69-73.
[19] Viberg M, Ottersten B, and Kailath T. Detection and estimation in sensor arrays using weighted subspace fitting[J]. IEEE Trans. on Signal Processing, 1991, 39(11): 2436-2449.
[20] Ziskind I and Wax M. Maximum likelihood localization of multiple sources byalternating projection[J]. IEEE Trans. on Acoustics, Speech, and Signal Processing, 1988, 36(10): 1553-1559.
[21]赵光辉,陈伯孝,董玫.基于交替投影的DOA估计方法及其在米波雷达中的应用[J].电子与信息学报, 2008, 30(1): 224-227.
[22] Lo T and Litva J. Low-angle tracking using a multifrequency sampled aperture radar[J]. IEEE Trans. on AES, 1991, 27(5): 797-805.
[23] Lo T and Litva J. Use of a highly deterministic multipath signal model in low-angle tracking[J]. IEE Proceedings-F, 1991, 138(2): 163-171.
[24] Lo T, Wong T, and Litva J. New technique for low-angle radar tracking[J]. Electronics Letters, 1991, 27(6): 529-531.
[25] Bosse E, Turner R M, and Brookes D. Improved radar tracking using a multipath model: maximum likelihood compared with eigenvector analysis[J]. IEE Proc.-Radar, Sonar, Navig. 1994, 141(4): 213-222.
[26] Mahafza B R. Radar systems analysis and design using MATLAB[M]. Chapman & Hall/CRC, 2000.
[27] Long M W.陆地和海面的雷达波散射特性[M].北京:科学出版社, 1981.
[28] Freehafer I E, Fishback W T, Furry W H, et al. Theory of propagation in a horizontal stratified atmosphere. in Kerr D. K. Propagation of short radio waves[M]. McCraw, 1952.