基于贝叶斯压缩感知的噪声MIMO雷达稳健目标参数提取方法  

A Robust Target Parameter Extraction Method via Bayesian Compressive Sensing for Noise MIMO Radar

作　　者：王超宇[1] 贺亚鹏[2] 朱晓华[1] 孙康[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》2013年第10期2498-2504,共7页Journal of Electronics & Information Technology

摘　　要：针对压缩感知雷达(Compressive Sensing Radar,CSR)面临测量噪声、信道干扰及系统精度误差等扰动时,非自适应随机测量值和感知矩阵失配导致传统CSR目标参数提取性能下降的问题,该文提出一种基于贝叶斯压缩感知(Bayesian Compressive Sensing,BCS)的噪声MIMO雷达稳健目标参数提取方法。文中首先建立了噪声MIMO雷达的稀疏感知模型,推导了基于目标参数稀疏贝叶斯模型的联合概率密度函数,随后将BCS方法与LASSO(Least-Absolute Shrinkage and Selection Operator)算法相结合对联合概率密度函数进行优化求解。与传统CSR算法相比,该方法能够在CSR系统模型存在失配误差时对目标参数进行有效估计,降低了目标参数估计误差,改善了CSR目标参数提取的准确性和鲁棒性。计算机仿真验证了该方法的有效性。This paper explores the theory of Compressive Sensing （CS） in radar and evaluates the perturbing effect on measurement noise, channel inference and radar system accuracy error. The performance of traditional Compressive Sensing Radar （CSR） are sensitivity to the above perturbations, which causing the mismatch between non-adaptive random measurement and sensing matrix. To solve the problem, a robust algorithm via Bayesian Compressive Sensing （BCS） with application to noise MIMO radar is proposed. First, a noise MIMO radar sparse sensing model is established and the jointly probability density function based on sparse Bayesian model is derived. Then the BCS algorithm and Least-Absolute Shrinkage and Selection Operator （LASSO） algorithm are employed to optimize the jointly probability density function. Comparing with traditional CSR algorithms, this method estimates effectively the parameters of target when existing mismatch in CSR model, reduces the target information estimation error, and enhances the accuracy and robustness of CSR target information extraction. The validity of the proposed method is illustrated by numerical example.

关 键 词：贝叶斯压缩感知 噪声MIMO雷达 感知矩阵 失配 

分 类 号：TN958[电子电信—信号与信息处理]