基于线性收缩的大阵列MIMO雷达目标盲检测

Linear shrinkage-based blind target detection for MIMO radar with large arrys

针对阵元数与快拍数可以相比拟的大阵列MIMO雷达系统,将协方差矩阵估计的收缩算法与大维随机矩阵理论相结合,提出了一种基于线性收缩-标准条件数(LS-SCN)的目标盲检测新方法。通过求解大维系统样本协方差矩阵的优化矩阵,并利用M-P律,推导了检测阈值与收缩系数之间的关系,分别给出了基于LS-SCN的单目标和多目标检测算法。该方法无需已知噪声方差、目标散射矩阵和目标方位等先验信息,对噪声变化不敏感,且适用于大阵列系统。仿真结果表明,在阵元数与快拍数在同一数量级的情况下,与SCN算法和MDL算法相比,显著提高了目标检测性能。

Aiming at Multiple Input Multiple Output （MIMO） radar system with large arrays, in which the number of arrays is comparable to the number of snapshots, a blind target detection method based on Linear Shrinkage-Standard Condition Number （LS-SCN） is proposed by combining the shrinkage algorithm of Covariance Matrix estimation and the large dimensional random matrix theory. By solving the optimization of the sample CM in the large dimensional regime and utilizing the M-P law, the relationship between the detection threshold and the shrinkage coefficient is derived. Single-target and multi-target detection algorithms based on LS-SCN are presented respectively. The method is not sensitive to noise changes and is suitable for large array system, which do not need to know the priori information of noise variance, target scattering matrix and target location. Simulation results show that, compared with SCN algorithm and Minimum Description Length （MDL） algorithm, the proposed methods significantly improve the performance of target detection under the circumstance that the numbers of arrays and snapshots grow at the same rate.