一种稳健的盲稀疏度压缩感知雷达目标参数估计方法  

A Robust Blind Sparsity Target Parameter Estimation Algorithm for Compressive Sensing Radar

作　　者：王超宇[1] 梅湄 朱晓华[1] 贺亚鹏[2] 李洪涛[1] 

机构地区：[1]南京理工大学电子工程与光电技术学院,南京210094 [2]中国空间技术研究院微波遥感与数传技术研究所,西安710000

出　　处：《电子与信息学报》2014年第4期960-966,共7页Journal of Electronics & Information Technology

摘　　要：针对压缩感知雷达(Compressive Sensing Radar,CSR)在感知矩阵和目标信息矢量失配时距离-多普勒参数估计性能下降的问题,该文提出一种稳健的盲稀疏度CSR目标参数估计方法。首先建立了CSR系统模型失配时的距离-多普勒2维参数稀疏感知模型,推导了以最小化感知矩阵相干系数(Coherence of Sensing Matrix,CSM)为准则的波形优化目标函数。其次提出了一种新的盲稀疏度CSR目标参数估计方法,通过发射波形,系统模型失配误差和目标信息矢量的相互迭代,逐步校正系统感知矩阵,最终以较高精度估计目标距离-多普勒参数。与传统CSR目标参数估计方法相比,该方法显著降低了CSR系统距离-多普勒参数的估计误差,改善了CSR目标参数估计的准确性和鲁棒性。计算机仿真验证了该方法的有效性。In order to enhance the performance of estimating range-Doppler parameters in presence of mismatch error between sensing matrix and target information vector for Compressive Sensing Radar （CSR）, a robust blind sparsity target parameter estimation algorithm is proposed. First, a two-dimensional sparse sensing model for range-Doppler estimation is established when there exists CSR system model mismatch error, and a waveform optimization object function is derived based on minimization Coherence of Sensing Matrix （CSM）. Then, a novel blind sparsity CSR algorithm is employed to correct system sensing matrix and estimate the range-Doppler parameters by optimizing iteratively transmit waveform, system mismatch error and target information vector. Compared with traditional CSR algorithm, the proposed method reduces the range-Doppler estimation error, and enhances the accuracy and robustness of CSR target information estimation. The validity of the proposed method is demonstrated with numerical simulation.

关 键 词：压缩感知雷达(CSR) 盲稀疏度 感知矩阵相干系数(CSM) 模拟退火(SA)算法 

分 类 号：TN958[电子电信—信号与信息处理]