基于压缩感知的分布式MIMO雷达速度估计与联合检测

Joint target velocity estimation and detection using distributed MIMO radar based on Compressed Sensing

针对分布式MIMO雷达系统,在站间大间隔配置获得空间分集增益的基础上,提出了一种基于压缩感知的目标速度估计与检测的联合算法,并利用二阶锥规划提高了感知算法在低信噪比时的估计精度。该算法有效降低了常规最大似然估计的计算成本,且在噪声功率未知时联合检测的性能上要明显优于距离门检测。通过理论分析证明,所提出的估计与联合检测算法能够有效估计速度并检测目标。仿真实验也验证了算法的有效性。

We consider multiple-input multiple-output（MIMO） radar system with widely spaced antennas,which facilitates capturing the inherent diversity gain for joint detection and estimation.A new algorithm for velocity estimation based on compressed sensing is present,which utilizes SOCP to improve the precision and avoids the high computational cost.Unlike conventional detected methods that break the space into small cells and aim at detecting the presence of a target in a specified cell,we treat this problem through offering a novel composite hypothesis testing framework for target detection when noise power is unknown.The analytical and empirical results show that the proposed algorithm is better than classical method for estimation and detection in MIMO radar.Simulation results verify the usefulness of the algorithm.