机载MIMO雷达稳健非均匀样本选择方法

针对杂波训练样本中混入干扰目标,导致空时自适应处理技术的杂波抑制性能下降问题,提出一种基于目标知识进行局部稀疏恢复的稳健训练样本挑选方法。该方法利用先验知识确定待检测单元中的目标区域,对整个角度-多普勒平面进行遍历,获得稀疏超完备基。通过变换矩阵对超完备基中对应的目标区域进行"挖空"处理,局部稀疏恢复出超分辨的杂波空时谱,获得杂波协方差矩阵估计。结合广义内积算法,实现非均匀训练样本挑选的过程。与常规结合广义内积方法相比,该方法对于不同干扰强度的训练样本,均有良好的检测效果。经仿真验证,所提方法的检验统计量之间区分度更加明显,对于干扰样本的挑选更加彻底,从而有效地提高了空时自适应处理技术的目标检测性能。

Airborne MIMO radar of robust non-homogeneous training sample detection method

Once clutter training samples mix with interfering targets, the clutter suppression performance by space-time adaptive processing will decline. In order to solve the problem, a robust training samples detection method based on target knowledge and partly sparse recovery was proposed. Firstly, the object region in unit to be detected was locked. Then the sparse complete base was obtained by covering the whole angle Doppler plane. After that, the corresponding object region in sparse complete base by transformation matrix was hollowed out to obtain the super resolution clutter space-time spectrum, which helps to estimate the clutter covariance matrix. Finally, the method was combined with the generalized inner product method to realize non-homogeneous training sample detection. Compared with GIP (generalized inner product) method, the proposed method can detect interfering targets in different intensity. Simulation analysis demonstrates that the test statistics of the proposed method have excellent discrimination validity, and can drastically eliminate interfering targets, thus improving the target detection performance of STAP (space-time adaptive processing).