一种快速的同时多机动目标参数估计方法
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摘要
近年来,对机动目标参数快速估计方法的研究受到了广泛关注。然而,许多已有的参数估计算法存在精度与计算量相矛盾的问题。此外,当同时对多个机动目标参数进行估计时,传统的时频类算法会存在交叉项的干扰。针对上述问题,注意到雷达回波信号的高阶相邻自相关函数(Higher-Order Adjacent Cross Correlation Function,HACCF)的展开式中,自相关项是与相邻延时无关的常数项,而交叉项则是以相邻延时为变量的函数。基于该特点,提出一种快速的估计多机动目标参数的估计方法。该方法先对信号的HACFF求均值以提取出自相关项,实现对交叉项的有效抑制;在此基础上进一步估计自相关项的频率,从而准确估计出机动目标的加速度等参数。与传统方法相比,该方法有如下优点:1)运算量小,能快速估计出机动目标参数;2)能同时估计出多个机动目标的参数;3)估计精度高。
In recent years,the study on fast estimation methods of maneuvering target parameters has attracted wide attention.However,many existing parameter estimation algorithms have many problems,such as the contradiction between precision and calculation quantity.In addition,when the parameters of multiple maneuvering targets are estimated at the same time,the traditional time-frequency algorithms have cross-term interference.According to the above problems,this paper notices the autocorrelation item is constant and independent of the adjacent time delay while the cross term is a function of the adjacent time delay in the higher-order adjacent autocorrelation function(HACCF)expansion of radar echo signal.On this basis,a fast parameter estimation method for multiple maneuvering targets is proposed in this paper.The method firstly takes the mean extraction of the signals HACFF to extract the auto items,and inhibits the cross term.Then we can estimate the frequency of auto items further and get accurate estimation of maneuvering target acceleration.Compared with traditional methods,this method has the following advantages:1)It has small calculation and can quickly estimate the maneuvering target parameters;2)The proposed algorithm can estimate the parameters of multiple maneuvering targets simultaneously;3)High accuracy.
In recent years,the study on fast estimation methods of maneuvering target parameters has attracted wide attention.However,many existing parameter estimation algorithms have many problems,such as the contradiction between precision and calculation quantity.In addition,when the parameters of multiple maneuvering targets are estimated at the same time,the traditional time-frequency algorithms have cross-term interference.According to the above problems,this paper notices the autocorrelation item is constant and independent of the adjacent time delay while the cross term is a function of the adjacent time delay in the higher-order adjacent autocorrelation function(HACCF)expansion of radar echo signal.On this basis,a fast parameter estimation method for multiple maneuvering targets is proposed in this paper.The method firstly takes the mean extraction of the signals HACFF to extract the auto items,and inhibits the cross term.Then we can estimate the frequency of auto items further and get accurate estimation of maneuvering target acceleration.Compared with traditional methods,this method has the following advantages:1)It has small calculation and can quickly estimate the maneuvering target parameters;2)The proposed algorithm can estimate the parameters of multiple maneuvering targets simultaneously;3)High accuracy.
引文
[1]HE Zhiqiang,PEI Jifang,YANG Haiguang,et al.A Fast Target Detection Framework for SAR Imagery[C]∥CIE International Conference on Radar,Guangzhou:IEEE,2016:1-4.
[2]CAO Zongjie,GE Yuchen,FENG Jilan.Fast Target Detection Method for High-Resolution SAR Images Based on Variance Weighted Information Entropy[J].EURASIP Journal on Advances in Signal Processing,2014,2014:45.
[3]LI Xiaolong,KONG Lingjiang,CUI Guolong,et al.A Fast Detection Method for Maneuvering Target in Coherent Radar[J].IEEE Sensors Journal,2015,15(11):6722-6729.
[4]李东,占木杨,方志平,等.基于HAF的参数化SAR宽带干扰抑制[J].系统工程与电子技术,2017,39(3):514-521.
[5]CHEN Xiaolong,GUAN Jian,HUANG Yong,et al.Radon-Linear Canonical Ambiguity Function-Based Detection and Estimation Method for Marine Target with Micromotion[J].IEEE Trans on Geoscience and Remote Sensing,2014,53(4):2225-2240.
[6]WANG Yong,KANG Jian,JIANG Yicheng.ISAR Imaging of Maneuvering Target Based on the Local Polynomial Wigner Distribution and Integrated HighOrder Ambiguity Function for Cubic Phase Signal Model[J].IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,2017,7(7):2971-2991.
[7]PACHORI R B,NISHAD A.Cross-Terms Reduction in the Wigner-Ville Distribution Using Tunable-Q Wavelet Transform[J].Signal Processing,2016,120:288-304.
[8]DONG Yuhua,CUI Yanqiu.Analysis of a New Joint Time-Frequency Distribution of Suppressing CrossTerm[J].Research Journal of Applied Sciences,Engineering and Technology,2012,4(11):1580-1584.
[9]KANEVA K I,LI Z.Multiple LFM Signals Detection Method Based on Pseudo-Wigner-Ville Distribution and Binary Integration of Hough Transform[C]∥International Multi Conference of Engineers and Computer Scientists,Hong Kong:[s.n.],2014:1-3.
[10]REDDY G R S, RAO R. Analysis of MultiComponent Non-Stationary Signals Using FourierBessel Series and Wigner-Hough Transform[J].Journal of Electronic Science and Technology of China,2017,15(1):69-76.
[11]JONEIDI M,ZAEEMZADEH A,REZAEIFAR S,et al.LFM Signal Detection and Estimation Based on Sparse Representation[C]∥49th Annual Conference on Information Sciences and Systems,Baltimore,MD:IEEE,2015:1-5.
[12]周阳,刘云清,初伟.基于FRFT的LFM信号的检测及其参数估计[J].湖南工业大学学报,2015,29(6):64-68.
[13]马垒,朱健东,赵拥军.LFMCW信号的周期FRFT域自适应检测与参数估计[J].信号处理,2015,31(1):103-110.
[14]CHEN Xiaolong,GUAN Jian,LIU Ningbo,et al.Maneuvering Target Detection via Radon-Fractional Fourier Transform-Based Long-Time Coherent Integration[J].IEEE Trans on Signal Processing,2014,62(4):939-953.
[15]LI Xiaolong,CUI Guolong,YI Wei,et al.A Fast Maneuvering Target Motion Parameters Estimation Algorithm Based on ACCF[J]. IEEE Signal Processing Letters,2015,22(3):270-274.
[16]ZHU Jiandong,LI Jinliang,GAO Xiangdong,et al.Adaptive Threshold Detection and Estimation of Linear Frequency-Modulated Continuous-Wave Signals Based on Periodic Fractional Fourier Transform[J].Circuits,Systems,and Signal Processing,2016,35(7):2502-2517.
[17]JIA Shuyi,WANG Guohong,ZHANG Y,et al.Resolution and Parameters Estimations for Multiple Maneuvering Targets[J].Science China:Information Sciences,2014,57:082312.
[18]李万阁,胡进峰,李强,等.OTHR低信噪比下的机动目标检测算法[J].雷达科学与技术,2014,12(4):373-378.LI Wange, HU Jinfeng, LI Qiang, et al.Maneuvering Target Detection Algorithm with Low Signal-to-Noise Ratio in OTH Radar[J].Radar Science and Technology,2014,12(4):373-378.(in Chinese)
[19]郭海燕,董云龙,关键.基于分数阶谱相减的弱目标检测方法[J].雷达科学与技术,2011,9(2):135-139.GUO Haiyan,DONG Yunlong,GUAN Jian.Weak Target Detection Based on Fractional Spectral Subtraction in Sea Clutter[J].Radar Science and Technology,2011,9(2):135-139.(in Chinese)
[2]CAO Zongjie,GE Yuchen,FENG Jilan.Fast Target Detection Method for High-Resolution SAR Images Based on Variance Weighted Information Entropy[J].EURASIP Journal on Advances in Signal Processing,2014,2014:45.
[3]LI Xiaolong,KONG Lingjiang,CUI Guolong,et al.A Fast Detection Method for Maneuvering Target in Coherent Radar[J].IEEE Sensors Journal,2015,15(11):6722-6729.
[4]李东,占木杨,方志平,等.基于HAF的参数化SAR宽带干扰抑制[J].系统工程与电子技术,2017,39(3):514-521.
[5]CHEN Xiaolong,GUAN Jian,HUANG Yong,et al.Radon-Linear Canonical Ambiguity Function-Based Detection and Estimation Method for Marine Target with Micromotion[J].IEEE Trans on Geoscience and Remote Sensing,2014,53(4):2225-2240.
[6]WANG Yong,KANG Jian,JIANG Yicheng.ISAR Imaging of Maneuvering Target Based on the Local Polynomial Wigner Distribution and Integrated HighOrder Ambiguity Function for Cubic Phase Signal Model[J].IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,2017,7(7):2971-2991.
[7]PACHORI R B,NISHAD A.Cross-Terms Reduction in the Wigner-Ville Distribution Using Tunable-Q Wavelet Transform[J].Signal Processing,2016,120:288-304.
[8]DONG Yuhua,CUI Yanqiu.Analysis of a New Joint Time-Frequency Distribution of Suppressing CrossTerm[J].Research Journal of Applied Sciences,Engineering and Technology,2012,4(11):1580-1584.
[9]KANEVA K I,LI Z.Multiple LFM Signals Detection Method Based on Pseudo-Wigner-Ville Distribution and Binary Integration of Hough Transform[C]∥International Multi Conference of Engineers and Computer Scientists,Hong Kong:[s.n.],2014:1-3.
[10]REDDY G R S, RAO R. Analysis of MultiComponent Non-Stationary Signals Using FourierBessel Series and Wigner-Hough Transform[J].Journal of Electronic Science and Technology of China,2017,15(1):69-76.
[11]JONEIDI M,ZAEEMZADEH A,REZAEIFAR S,et al.LFM Signal Detection and Estimation Based on Sparse Representation[C]∥49th Annual Conference on Information Sciences and Systems,Baltimore,MD:IEEE,2015:1-5.
[12]周阳,刘云清,初伟.基于FRFT的LFM信号的检测及其参数估计[J].湖南工业大学学报,2015,29(6):64-68.
[13]马垒,朱健东,赵拥军.LFMCW信号的周期FRFT域自适应检测与参数估计[J].信号处理,2015,31(1):103-110.
[14]CHEN Xiaolong,GUAN Jian,LIU Ningbo,et al.Maneuvering Target Detection via Radon-Fractional Fourier Transform-Based Long-Time Coherent Integration[J].IEEE Trans on Signal Processing,2014,62(4):939-953.
[15]LI Xiaolong,CUI Guolong,YI Wei,et al.A Fast Maneuvering Target Motion Parameters Estimation Algorithm Based on ACCF[J]. IEEE Signal Processing Letters,2015,22(3):270-274.
[16]ZHU Jiandong,LI Jinliang,GAO Xiangdong,et al.Adaptive Threshold Detection and Estimation of Linear Frequency-Modulated Continuous-Wave Signals Based on Periodic Fractional Fourier Transform[J].Circuits,Systems,and Signal Processing,2016,35(7):2502-2517.
[17]JIA Shuyi,WANG Guohong,ZHANG Y,et al.Resolution and Parameters Estimations for Multiple Maneuvering Targets[J].Science China:Information Sciences,2014,57:082312.
[18]李万阁,胡进峰,李强,等.OTHR低信噪比下的机动目标检测算法[J].雷达科学与技术,2014,12(4):373-378.LI Wange, HU Jinfeng, LI Qiang, et al.Maneuvering Target Detection Algorithm with Low Signal-to-Noise Ratio in OTH Radar[J].Radar Science and Technology,2014,12(4):373-378.(in Chinese)
[19]郭海燕,董云龙,关键.基于分数阶谱相减的弱目标检测方法[J].雷达科学与技术,2011,9(2):135-139.GUO Haiyan,DONG Yunlong,GUAN Jian.Weak Target Detection Based on Fractional Spectral Subtraction in Sea Clutter[J].Radar Science and Technology,2011,9(2):135-139.(in Chinese)