声矢量传感器稳健空间谱估计技术研究
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摘要
在传统的声矢量传感器空间谱估计方法中，常常假定具备理想的阵列孔径、阵列流型模型和接收数据协方差矩阵估计模型条件，然而在实际应用中，上述假设条件往往转化为模型失配的非理想条件。本文根据声矢量传感器技术的最新发展趋势，结合实际的应用环境，针对阵列孔径受限、阵列流型模型失配、接收数据协方差矩阵估计模型失配等非理想条件下矢量阵稳健空间谱估计的热点和难点，依据国内外研究现状，对声矢量传感器稳健空间谱估计问题开展了研究，主要研究内容包括：
1.对于声矢量传感器阵列而言，其阵列孔径越大分辨能力就会越强。然而实际应用中，受安装平台、经济性、安全性等条件制约往往只能使用小孔径阵列。针对阵列孔径受限条件下的声矢量传感器空间谱估计问题，提出了一种声矢量传感器阵列虚拟扩展技术，详细探讨了虚拟扩展前后矢量阵列波束宽度、主旁瓣特性、处理增益和噪声特性的变化。
为提高小孔径声矢量传感器阵列空间谱估计性能，提出一种基于声矢量传感器的时空变换信号处理方法。该方法考虑声矢量传感器的结构特性，利用各向同性声场中信号与噪声相关性差异和时空相关半径的不同，将声矢量传感器各通道时域采样转化成空域快拍，进而将小孔径声矢量传感器阵转化成多阵元矢量阵列。方法对于非相干宽/窄带信号均具有鲁棒性，依靠单个声矢量传感器即可完成对双目标的分辨。理论分析、仿真试验和湖上试验及消声水池试验结果验证了方法的有效性。
2.对于众多声矢量传感器阵列空间谱估计算法而言，常常假定精确已知阵列流型，即阵列的阵元位置已知，通道幅度相位具有较好的一致性，阵元不存在矢量姿态误差等。现实应用中，由于安装位置、制作工艺、水文条件等的影响，上述理想条件往往不复存在，进而使得大部分空间谱估计算法性能受到严重影响。针对这样的问题，建立了声矢量传感器阵元位置误差、通道幅相响应误差和矢量姿态误差的数学模型，分析了上述误差对于矢量空间谱估计的影响。对于存在阵元位置误差或通道幅相响应误差的情况，提出一种有源误差校正方法。利用三个频率已知的辅助信源分时发送校正信号，通过求解阵元间接收数据的方程对上述误差进行校正。对于存在矢量姿态误差的情况，提出了一种基于正交加权能量检测的误差校正方法。通过对声矢量传感器的振速两通道进行正交加权，利用信噪能量差异完成误差校正。仿真结果验证了上述算法的有效性。
3.声矢量传感器阵列信号处理应用环境中，往往存在对于高速运动目标或瞬态信号方位估计的需求。对于上述目标而言，在一个信号处理周期内，其可获得的空间采样快拍数往往是受限的，这将严重影响对于接收数据协方差矩阵的估计，进而影响空间谱估计性能。针对由高速运动目标或瞬态信号导致的小快拍（单快拍）条件下的空间谱估计问题，提出了一种基于压缩感知技术的时空联合滤波空间谱估计方法。方法具有较高的估计精度和较强的鲁棒性。仿真试验及湖上试验结果验证了方法的有效性。
In the conventional spatial spectrum estimation methods field for acoustic vector sensor,it is often considered that the sonar system has an ideal model of array aperture, arraymanifold model and receiving data covariance matrix for estimation. In practical applications,however, the above assumptions are often converted to model mismatch of non-idealconditions. According to the latest trends of acoustic vector sensor technology combined withthe actual application environment, the dissertation focuses on the robust space spectrumestimation problem under the limited array aperture, array manifold model mismatch andreceiving data covariance matrix estimation model mismatch non-ideal conditions with thenewest research results home and abroad. The key research results in this dissertation include:
1. The resolving ability of an acoustic vector sensor array becomes stronger when theaperture of the array is broader. However, due to the consideration of the mounting platform,economy, and security in the practical application, a small aperture array is often adopted. Tothe problem of acoustic vector sensor array spatial spectrum estimation with a limitedaperture array, an acoustic vector sensor array virtual extension technology is proposed.Discussion in detail shows the changes of the array beam width, main sidelobe ratio，arrayprocessing gain, and the noise characteristics before and after a virtual extension of the vectorarray.
To improve the performance of small aperture acoustic vector sensor array spatialspectrum, a novel time-space transform signal processing approach based on acoustic vectorsensor is proposed. The method takes the structural characteristics of the acoustic vectorsensor into account and uses the difference of space-time correlation radius of signal andnoise in the isotropic acoustic field, then putting each acoustic vector sensor channel timedomain samples into the snapshots in spatial field, in which the small aperture acoustic vectorsensor array is changed into an array of multiple vector elements. When using this newmethod, noncoherent wide/narrow-band signal is robust for spatial spectrum estimation. TheDOAs of dual objectives can be obtained relying on only a single acoustic vector sensor.Theoretical analysis, simulation and test results in lake and anechoic tank verify theeffectiveness of the method.
2. For many acoustic vector sensor array space spectrum estimation algorithms, arraymanifold is often considered as priori knowledge, which includes a known array unit position,consistency of channel amplitude-phase response and calibrated units with no vector attitude error. However, the ideal conditions above do not exist due to the installation location,craftsmanship and hydrological conditions, which make performances of most of the spatialspectrum estimation algorithms affected severely. For the problems mentioned above,acoustic vector sensor array position error, amplitude and phase response error and vectorattitude error models are founded. Analysis of the impact of the error above to vector spatialspectrum estimation is given out. For the presence of sensor position errors or channelamplitude-phase response errors, an active error calibration method is proposed. The methoduses three auxiliary signal sources with known frequency to send calibration signals in timesequence. The error calibration is done by solving the equation of the received data in thearray element. For the presence of vector attitude error, an error calibration algorithm basedon orthogonal weighted energy detection is proposed. The algorithm weights on the twochannels of velocity, and thus uses the energy difference of signal and noise to complete errorcalibration. The simulation results verify the validity of the above algorithms.
3. In acoustic vector sensor array signal processing applications environment, a demandin estimating the azimuth of high-speed moving targets or transient signal is existed. For theabove-mentioned objectives, the spatial sample snapshots number is often limited in onesignal processing time cycle, which will seriously affect the estimation for receiving datacovariance matrix, thereby affecting the spatial spectrum estimation performance. To solvethe problem of spatial spectrum estimation with small snapshots (single snapshot) caused byhigh-speed moving targets or transient signal, a compressed sensing technology based jointspatial and temporal filtering spatial spectrum estimation method is proposed. The method hashigh estimation accuracy and a strong robust ability. The simulation and lake test resultsverify the effectiveness of the method.
1.对于声矢量传感器阵列而言，其阵列孔径越大分辨能力就会越强。然而实际应用中，受安装平台、经济性、安全性等条件制约往往只能使用小孔径阵列。针对阵列孔径受限条件下的声矢量传感器空间谱估计问题，提出了一种声矢量传感器阵列虚拟扩展技术，详细探讨了虚拟扩展前后矢量阵列波束宽度、主旁瓣特性、处理增益和噪声特性的变化。
为提高小孔径声矢量传感器阵列空间谱估计性能，提出一种基于声矢量传感器的时空变换信号处理方法。该方法考虑声矢量传感器的结构特性，利用各向同性声场中信号与噪声相关性差异和时空相关半径的不同，将声矢量传感器各通道时域采样转化成空域快拍，进而将小孔径声矢量传感器阵转化成多阵元矢量阵列。方法对于非相干宽/窄带信号均具有鲁棒性，依靠单个声矢量传感器即可完成对双目标的分辨。理论分析、仿真试验和湖上试验及消声水池试验结果验证了方法的有效性。
2.对于众多声矢量传感器阵列空间谱估计算法而言，常常假定精确已知阵列流型，即阵列的阵元位置已知，通道幅度相位具有较好的一致性，阵元不存在矢量姿态误差等。现实应用中，由于安装位置、制作工艺、水文条件等的影响，上述理想条件往往不复存在，进而使得大部分空间谱估计算法性能受到严重影响。针对这样的问题，建立了声矢量传感器阵元位置误差、通道幅相响应误差和矢量姿态误差的数学模型，分析了上述误差对于矢量空间谱估计的影响。对于存在阵元位置误差或通道幅相响应误差的情况，提出一种有源误差校正方法。利用三个频率已知的辅助信源分时发送校正信号，通过求解阵元间接收数据的方程对上述误差进行校正。对于存在矢量姿态误差的情况，提出了一种基于正交加权能量检测的误差校正方法。通过对声矢量传感器的振速两通道进行正交加权，利用信噪能量差异完成误差校正。仿真结果验证了上述算法的有效性。
3.声矢量传感器阵列信号处理应用环境中，往往存在对于高速运动目标或瞬态信号方位估计的需求。对于上述目标而言，在一个信号处理周期内，其可获得的空间采样快拍数往往是受限的，这将严重影响对于接收数据协方差矩阵的估计，进而影响空间谱估计性能。针对由高速运动目标或瞬态信号导致的小快拍（单快拍）条件下的空间谱估计问题，提出了一种基于压缩感知技术的时空联合滤波空间谱估计方法。方法具有较高的估计精度和较强的鲁棒性。仿真试验及湖上试验结果验证了方法的有效性。
In the conventional spatial spectrum estimation methods field for acoustic vector sensor,it is often considered that the sonar system has an ideal model of array aperture, arraymanifold model and receiving data covariance matrix for estimation. In practical applications,however, the above assumptions are often converted to model mismatch of non-idealconditions. According to the latest trends of acoustic vector sensor technology combined withthe actual application environment, the dissertation focuses on the robust space spectrumestimation problem under the limited array aperture, array manifold model mismatch andreceiving data covariance matrix estimation model mismatch non-ideal conditions with thenewest research results home and abroad. The key research results in this dissertation include:
1. The resolving ability of an acoustic vector sensor array becomes stronger when theaperture of the array is broader. However, due to the consideration of the mounting platform,economy, and security in the practical application, a small aperture array is often adopted. Tothe problem of acoustic vector sensor array spatial spectrum estimation with a limitedaperture array, an acoustic vector sensor array virtual extension technology is proposed.Discussion in detail shows the changes of the array beam width, main sidelobe ratio，arrayprocessing gain, and the noise characteristics before and after a virtual extension of the vectorarray.
To improve the performance of small aperture acoustic vector sensor array spatialspectrum, a novel time-space transform signal processing approach based on acoustic vectorsensor is proposed. The method takes the structural characteristics of the acoustic vectorsensor into account and uses the difference of space-time correlation radius of signal andnoise in the isotropic acoustic field, then putting each acoustic vector sensor channel timedomain samples into the snapshots in spatial field, in which the small aperture acoustic vectorsensor array is changed into an array of multiple vector elements. When using this newmethod, noncoherent wide/narrow-band signal is robust for spatial spectrum estimation. TheDOAs of dual objectives can be obtained relying on only a single acoustic vector sensor.Theoretical analysis, simulation and test results in lake and anechoic tank verify theeffectiveness of the method.
2. For many acoustic vector sensor array space spectrum estimation algorithms, arraymanifold is often considered as priori knowledge, which includes a known array unit position,consistency of channel amplitude-phase response and calibrated units with no vector attitude error. However, the ideal conditions above do not exist due to the installation location,craftsmanship and hydrological conditions, which make performances of most of the spatialspectrum estimation algorithms affected severely. For the problems mentioned above,acoustic vector sensor array position error, amplitude and phase response error and vectorattitude error models are founded. Analysis of the impact of the error above to vector spatialspectrum estimation is given out. For the presence of sensor position errors or channelamplitude-phase response errors, an active error calibration method is proposed. The methoduses three auxiliary signal sources with known frequency to send calibration signals in timesequence. The error calibration is done by solving the equation of the received data in thearray element. For the presence of vector attitude error, an error calibration algorithm basedon orthogonal weighted energy detection is proposed. The algorithm weights on the twochannels of velocity, and thus uses the energy difference of signal and noise to complete errorcalibration. The simulation results verify the validity of the above algorithms.
3. In acoustic vector sensor array signal processing applications environment, a demandin estimating the azimuth of high-speed moving targets or transient signal is existed. For theabove-mentioned objectives, the spatial sample snapshots number is often limited in onesignal processing time cycle, which will seriously affect the estimation for receiving datacovariance matrix, thereby affecting the spatial spectrum estimation performance. To solvethe problem of spatial spectrum estimation with small snapshots (single snapshot) caused byhigh-speed moving targets or transient signal, a compressed sensing technology based jointspatial and temporal filtering spatial spectrum estimation method is proposed. The method hashigh estimation accuracy and a strong robust ability. The simulation and lake test resultsverify the effectiveness of the method.
引文
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