机载雷达SAR/GMTI及非正侧视线阵STAP技术研究
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摘要
地面运动目标指示(Ground Moving Target Indication，GMTI)作为战场侦察的一部分是军用机载监视雷达所必须具备的一项基本功能，也是雷达信号处理领域中的一个重要问题。本文的研究工作主要围绕多通道SAR/GMTI技术以及非正侧视阵列结构下的STAP技术展开，主要内容及创新点如下：
1.研究了基于样本协方差矩阵特征分解的双通道SAR动目标检测技术。传统的基于样本协方差矩阵特征分解得到的动目标检测量如第二特征值、干涉相位及相似度，由于只利用了协方差矩阵的幅度和相位的单方面信息来实现慢动目标的检测，因而其检测效果并不理想。为此，本文在样本协方差矩阵特征分解的基础上，通过对上述三个参量进行有效组合，提出了两种新的动目标检测算法——椭圆检测算法和第二特征值—干涉相位联合检测算法。前者是通过对干涉相位和相似度进行变换得到的动目标检测量；后者是将杂波第二特征值和干涉相位联合统计特性的研究结果用于慢动目标的检测，与此同时，还结合了第二特征值、干涉相位门限预处理。文中通过仿真及实测数据实验对上述几种动目标算法的性能进行了验证与对比。
2.对多通道SAR/GMTI通道盲均衡处理技术进行了研究。首先，建立了多通道SAR/GMTI回波信号模型；接着，对基于回波数据相关矩阵特征分解的通道盲均衡算法的基本工作原理进行分析，分析结果表明，该算法主要存在两个缺点：一是收敛速度慢；二是算法的有效性受干扰信号的影响较大。针对算法收敛速度慢的缺点，论文提出了一种快速收敛的通道盲均衡算法。实测数据的实验结果表明，该算法可有效减少收敛所需的样本数目，但与原通道盲均衡算一样，该算法的有效性同样会受干扰信号的影响。基于此，在随后的研究中，将中值估计方法引入到该算法中，又提出了一种新的对干扰信号鲁棒的通道盲均衡算法，文中实测数据的实验结果验证了新算法的有效性。
3.研究了多通道SAR动目标检测技术以及速度估计。针对机载多通道SAR/GMTI系统及实测数据，提出一种新的地面慢动目标检测算法。新算法利用多级维纳滤波器实现多通道SAR系统杂波抑制，同时结合对角加载技术和改进的自适应功率剩余非均匀检测器，进一步改善SAR系统在非均匀环境下的动目标检测性能，最后通过实测数据实验对该算法的有效性和优越性进行了验证。随后，介绍了两种动目标径向速度估计方法，分别是最大似然估计方法和基于稀疏恢复的动目标径向速度估计方法，后者是新提出的一种动目标径向速度估计方法，该方法将稀疏恢复理论应用到动目标的径向速度估计中去，目的是为了提高系统对动目标径向速度的估计精度。文中给出了上述两种动目标径向速度估计方法的实测数据处理结果。
4.研究了非正侧视阵列结构下的STAP技术。首先，分析了非正侧视阵列结构下杂波的分布特性；接着，对STAP技术的基本工作原理进行了研究；随后，分析了两种非自适应杂波距离相关性补偿算法——多普勒频移补偿算法和角度—多普勒补偿算法，并通过仿真实验对两种算法的补偿性能进行了对比分析；在这之后，介绍了两种自适应杂波距离相关性补偿算法——自适应角度—多普勒补偿算法和快速自适应角度—多普勒补偿算法，后者是本文新提出的一种补偿算法，它将分块处理和近似投影子空间跟踪技术引入到原自适应角度—多普勒补偿算法中，从而来减少原算法的运算量。文中通过仿真实验对两种自适应杂波距离相关性补偿算法的性能进行对比分析。
Ground Moving Target Indication (GMTI) as a part of tactical reconnaissance is a necessaryfunction of the airborne surveillance radar for military application, and is a hot issue in the field ofradar signal processing. This thesis mainly investigates multi-channel Synthetic Aperture RadarGround Moving Target Indication (SAR/GMTI) technique and Space Time Adaptive Processing(STAP) technique for non-sidelooking Uniform Linear Array (ULA). The major work can besummarized as follows:
1. Two-channel SAR/GMTI techniques based on eigen-decomposition of the covariancematrix are investigated. The previous studies turn out that the decomposition elements such as thesecond eigenvalue, the Along-Track Interferometric (ATI) phase and the similarity can be used asGMTI metrics. However, unfortunately, their GMTI performance is so low since those threemetrics only utilize the phase or amplitude information of the SAR image pair to detect movingtargets. To further enhance GMTI performance, two new composite metrics are introduced and areapplied to detect moving targets. One is called ellipse detector, jointing the similarity and the ATIphase information together and changing the two random variables into one random variable. Theother examines the statistic of the second eigenvalue and the ATI phase for ground moving targetindication. Based on this statistic, a Constant False Alarm Rate (CFAR) detector can be designedto solve the problem of GMTI. To detect slow moving targets more accurately, the secondeigenvalue and the ATI phase pre-thresholds are implemented before this CFAR detector. Finally,the detection capability of the two proposed methods is demonstrated by numerical experimentson simulated data and measured SAR data.
2. Channel blind equalization techniques for multi-channel SAR/GMTI system areresearched. First, the echo model for multi-channel SAR/GMTI is built up. Second, the principleof channel blind equalization algorithm based on Eigen-Decomposition of data covariance matrixis investigated. However, it turns out that this algorithm has two fatal disadvantages. One is that itsuffers from a slow convergence rate. The other is that the effectiveness of this algorithm isseriously influenced by the moving target signal in training samples. Third, to improve itsconvergence rate, reduced-dimension technique is used into this algorithm and a new channelblind equalization algorithm is proposed. Experimental results on measured SAR data demonstratethat the proposed algorithm shows a fast convergence rate and is able to calibrate channelmismatch with much less sample support. However, unfortunately, this new algorithm is the sameto old one, and its effectiveness is also influenced by the moving target signal in training samples.Fourth, in order to enhance the robustness of the algorithm to moving target signal, median estimate is applied to the proposed algorithm. Finally, the validity of modified algorithm isdemonstrated by measured SAR data.
3. Multi-channel SAR ground moving target detection and radial velocity estimation areinvestigated. At first, a novel approach to moving target detection is proposed for Multi-channelSAR system. This approach utilizes Multistage Wiener Filter to suppress clutter. To improveGMTI performance in heterogeneous clutter environment, this new approach also combinesDiagonal Loading (DL) techniques and modified Adaptive Power Residual Non-HomogeneityDetector (APR-NHD). Numerical experiments on measured SAR data are presented todemonstrate the validity and advantage of this new algorithm. Subsequently, two methods toestimate moving target’s radial velocity are introduced. One is the maximum likelihood estimation.The other is to use spare signal reconstruction for moving target’s velocity estimation. Comparedwith the former, the latter improves estimation precision of moving target’s velocity estimation.Finally, the validity of two estimation methods is demonstrated by Numerical experiments onmeasured SAR data.
4. STAP technique for non-sidelooking ULA is researched. First, space-time two-dimensionaldistribution of clutter spectrum in non-sidelooking ULA is analyzed. Second, two non-adaptivecompensation methods for clutter range dependence such as Doppler Warping (DW) method andAngle Doppler Compensation (ADC) method are introduced. Meanwhile, Numerical experimentson simulation data are presented to analyze and compare compensation performance of twomethods. Third, two adaptive compensation methods for clutter range dependence such asAdaptive Angle Doppler Compensation (A2DC) method and Fast Adaptive Angle DopplerCompensation (FA2DC) method are introduced. FA2DC method is a new adaptive compensationmethod. In order to address the computational burden of A2DC method, FA2DC method insertsblock processing and Projection Approximation Subspace Tracking (PAST) technique into A2DCmethod. Finally, Numerical experiments on simulation data are presented to analyze and comparecompensation performance of two methods for clutter range dependence.
1.研究了基于样本协方差矩阵特征分解的双通道SAR动目标检测技术。传统的基于样本协方差矩阵特征分解得到的动目标检测量如第二特征值、干涉相位及相似度，由于只利用了协方差矩阵的幅度和相位的单方面信息来实现慢动目标的检测，因而其检测效果并不理想。为此，本文在样本协方差矩阵特征分解的基础上，通过对上述三个参量进行有效组合，提出了两种新的动目标检测算法——椭圆检测算法和第二特征值—干涉相位联合检测算法。前者是通过对干涉相位和相似度进行变换得到的动目标检测量；后者是将杂波第二特征值和干涉相位联合统计特性的研究结果用于慢动目标的检测，与此同时，还结合了第二特征值、干涉相位门限预处理。文中通过仿真及实测数据实验对上述几种动目标算法的性能进行了验证与对比。
2.对多通道SAR/GMTI通道盲均衡处理技术进行了研究。首先，建立了多通道SAR/GMTI回波信号模型；接着，对基于回波数据相关矩阵特征分解的通道盲均衡算法的基本工作原理进行分析，分析结果表明，该算法主要存在两个缺点：一是收敛速度慢；二是算法的有效性受干扰信号的影响较大。针对算法收敛速度慢的缺点，论文提出了一种快速收敛的通道盲均衡算法。实测数据的实验结果表明，该算法可有效减少收敛所需的样本数目，但与原通道盲均衡算一样，该算法的有效性同样会受干扰信号的影响。基于此，在随后的研究中，将中值估计方法引入到该算法中，又提出了一种新的对干扰信号鲁棒的通道盲均衡算法，文中实测数据的实验结果验证了新算法的有效性。
3.研究了多通道SAR动目标检测技术以及速度估计。针对机载多通道SAR/GMTI系统及实测数据，提出一种新的地面慢动目标检测算法。新算法利用多级维纳滤波器实现多通道SAR系统杂波抑制，同时结合对角加载技术和改进的自适应功率剩余非均匀检测器，进一步改善SAR系统在非均匀环境下的动目标检测性能，最后通过实测数据实验对该算法的有效性和优越性进行了验证。随后，介绍了两种动目标径向速度估计方法，分别是最大似然估计方法和基于稀疏恢复的动目标径向速度估计方法，后者是新提出的一种动目标径向速度估计方法，该方法将稀疏恢复理论应用到动目标的径向速度估计中去，目的是为了提高系统对动目标径向速度的估计精度。文中给出了上述两种动目标径向速度估计方法的实测数据处理结果。
4.研究了非正侧视阵列结构下的STAP技术。首先，分析了非正侧视阵列结构下杂波的分布特性；接着，对STAP技术的基本工作原理进行了研究；随后，分析了两种非自适应杂波距离相关性补偿算法——多普勒频移补偿算法和角度—多普勒补偿算法，并通过仿真实验对两种算法的补偿性能进行了对比分析；在这之后，介绍了两种自适应杂波距离相关性补偿算法——自适应角度—多普勒补偿算法和快速自适应角度—多普勒补偿算法，后者是本文新提出的一种补偿算法，它将分块处理和近似投影子空间跟踪技术引入到原自适应角度—多普勒补偿算法中，从而来减少原算法的运算量。文中通过仿真实验对两种自适应杂波距离相关性补偿算法的性能进行对比分析。
Ground Moving Target Indication (GMTI) as a part of tactical reconnaissance is a necessaryfunction of the airborne surveillance radar for military application, and is a hot issue in the field ofradar signal processing. This thesis mainly investigates multi-channel Synthetic Aperture RadarGround Moving Target Indication (SAR/GMTI) technique and Space Time Adaptive Processing(STAP) technique for non-sidelooking Uniform Linear Array (ULA). The major work can besummarized as follows:
1. Two-channel SAR/GMTI techniques based on eigen-decomposition of the covariancematrix are investigated. The previous studies turn out that the decomposition elements such as thesecond eigenvalue, the Along-Track Interferometric (ATI) phase and the similarity can be used asGMTI metrics. However, unfortunately, their GMTI performance is so low since those threemetrics only utilize the phase or amplitude information of the SAR image pair to detect movingtargets. To further enhance GMTI performance, two new composite metrics are introduced and areapplied to detect moving targets. One is called ellipse detector, jointing the similarity and the ATIphase information together and changing the two random variables into one random variable. Theother examines the statistic of the second eigenvalue and the ATI phase for ground moving targetindication. Based on this statistic, a Constant False Alarm Rate (CFAR) detector can be designedto solve the problem of GMTI. To detect slow moving targets more accurately, the secondeigenvalue and the ATI phase pre-thresholds are implemented before this CFAR detector. Finally,the detection capability of the two proposed methods is demonstrated by numerical experimentson simulated data and measured SAR data.
2. Channel blind equalization techniques for multi-channel SAR/GMTI system areresearched. First, the echo model for multi-channel SAR/GMTI is built up. Second, the principleof channel blind equalization algorithm based on Eigen-Decomposition of data covariance matrixis investigated. However, it turns out that this algorithm has two fatal disadvantages. One is that itsuffers from a slow convergence rate. The other is that the effectiveness of this algorithm isseriously influenced by the moving target signal in training samples. Third, to improve itsconvergence rate, reduced-dimension technique is used into this algorithm and a new channelblind equalization algorithm is proposed. Experimental results on measured SAR data demonstratethat the proposed algorithm shows a fast convergence rate and is able to calibrate channelmismatch with much less sample support. However, unfortunately, this new algorithm is the sameto old one, and its effectiveness is also influenced by the moving target signal in training samples.Fourth, in order to enhance the robustness of the algorithm to moving target signal, median estimate is applied to the proposed algorithm. Finally, the validity of modified algorithm isdemonstrated by measured SAR data.
3. Multi-channel SAR ground moving target detection and radial velocity estimation areinvestigated. At first, a novel approach to moving target detection is proposed for Multi-channelSAR system. This approach utilizes Multistage Wiener Filter to suppress clutter. To improveGMTI performance in heterogeneous clutter environment, this new approach also combinesDiagonal Loading (DL) techniques and modified Adaptive Power Residual Non-HomogeneityDetector (APR-NHD). Numerical experiments on measured SAR data are presented todemonstrate the validity and advantage of this new algorithm. Subsequently, two methods toestimate moving target’s radial velocity are introduced. One is the maximum likelihood estimation.The other is to use spare signal reconstruction for moving target’s velocity estimation. Comparedwith the former, the latter improves estimation precision of moving target’s velocity estimation.Finally, the validity of two estimation methods is demonstrated by Numerical experiments onmeasured SAR data.
4. STAP technique for non-sidelooking ULA is researched. First, space-time two-dimensionaldistribution of clutter spectrum in non-sidelooking ULA is analyzed. Second, two non-adaptivecompensation methods for clutter range dependence such as Doppler Warping (DW) method andAngle Doppler Compensation (ADC) method are introduced. Meanwhile, Numerical experimentson simulation data are presented to analyze and compare compensation performance of twomethods. Third, two adaptive compensation methods for clutter range dependence such asAdaptive Angle Doppler Compensation (A2DC) method and Fast Adaptive Angle DopplerCompensation (FA2DC) method are introduced. FA2DC method is a new adaptive compensationmethod. In order to address the computational burden of A2DC method, FA2DC method insertsblock processing and Projection Approximation Subspace Tracking (PAST) technique into A2DCmethod. Finally, Numerical experiments on simulation data are presented to analyze and comparecompensation performance of two methods for clutter range dependence.
引文
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