基于贝叶斯压缩感知的噪声MIMO雷达目标成像  

Noise MIMO radar target imaging based on Bayesian compressive sensing

作　　者：王超宇[1] 贺亚鹏[1] 胡恒[1] 朱晓华[1] 

机构地区：[1]南京理工大学电子工程与光电技术学院,江苏南京210094

出　　处：《南京理工大学学报》2013年第2期262-268,共7页Journal of Nanjing University of Science and Technology

基　　金：南京理工大学自主科研专项计划(2010ZDJH05)

摘　　要：为了提高低信噪比下压缩感知雷达的成像性能,该文提出了一种基于贝叶斯压缩感知的噪声多入多出(MIMO)雷达成像方法。给出了噪声MIMO雷达系统稀疏感知模型,构造了贝叶斯概率密度函数,利用最大后验概率优化方法对目标函数进行优化求解。优化估计的结果接近最佳稀疏度,与传统压缩感知重构方法相比,该方法能够有效降低目标场景向量的估计误差,提高目标二维像的质量,对噪声干扰的鲁棒性更好。仿真结果验证了该方法的有效性。To enhance the performance of the compressive sensing radar imaging in the low signal to noise ratio,the noise multiple input multiple output(MIMO)radar target imaging based on the Bayesian compressive sensing(BCS)is proposed.The sparse sensing model of the noise MIMO radar and the Bayesian probability density function are presented,and an optimization method based on maximum a posteriori is employed to solve the above problem.The estimate signal vector of the target scene closes to the best optimize results.Compared with the traditional compressed sensing reconstruction method,the proposed method can effectively reduce errors of the estimate,improve the quality of the two dimensional image,and show the better robustness to noise.Simulation results demonstrate the effectiveness of the method.

关 键 词：贝叶斯压缩感知 噪声多入多出雷达 目标成像 

分 类 号：TN958.8[电子电信—信号与信息处理]