基于加权SPICE的MIMO-STAP稀疏恢复算法

Sparse Recovery Algorithm of MIMO-STAP Based on Weighted SPICE

针对机载多输入多输出(MIMO)雷达空时自适应处理(STAP)技术在非均匀杂波条件下动目标检测性能严重下降的问题,引入了加权SPICE算法用于杂波谱的稀疏恢复。加权SPICE算法可以将一大类稀疏恢复算法纳入到统一框架下,根据加权矢量不同可得LIKES,SLIM和IAA算法。这些算法不需要设置任何超参数,基于杂波样本协方差矩阵通过迭代求解未知稀疏参数。仿真实验表明,使用这些稀疏算法恢复杂波谱,可有效提升所恢复杂波谱的准确性,能够更好地实现动目标检测。

Aiming at the problem that the performance of airborne multi-input multi-output(MIMO)radar space-time adaptive processing(STAP)technology is seriously degraded in the condition of nonhomogeneous clutter,the weighted SPICE sparse algorithm is introduced to recover the clutter spectrum.Weighted SPICE algorithm can incorporate a large class of sparse recovery algorithms into a unified framework,and LIKES,SLIM,IAA algorithms can be obtained according to different weighted vectors.These algorithms do not need to set any hyperparameter,and the unknown sparse parameters can be obtained through repeated iteration based on the clutter covariance matrix of the training sample.The simulation experiment shows that using these sparse algorithms to recover the clutter spectrum can effectively improve the accuracy of the recovered clutter spectrum,and can better realize the moving target detection.