非均匀环境下基于知识辅助的扩展目标Wald检测器

针对分布式多输入多输出（Multiple Input Multiple Output, MIMO）雷达中运动扩展目标的检测问题，本文首先假设每个发射-接收天线组的干扰信号协方差矩阵为互不相同的随机矩阵，以模拟实际的非均匀工作环境。然后引入知识辅助模型，建立先验信息矩阵，描述非均匀环境下的干扰信号特性，其中所有发射-接收天线组的干扰协方差矩阵服从以先验信息矩阵为基础的逆Wishart分布。在此基础上，设计了一种基于知识辅助的Wald（KA-Wald）检测器。仿真实验表明，在小样本的情况下，本文设计的KA-Wald检测器在检测性能上优于传统Wald检测器。而与已有的基于知识辅助的广义似然比检验（KA-GLRT）检测器相比，检测性能相近，但是计算效率更高。 

Knowledge-Aided Wald detector for Range-Extended Target in Nonhomogeneous Environments

This paper deals with the problem of detecting the moving range-extended target in the distributed MIMO radar. Firstly, the interference covariance matrices corresponding to different transmit-receive (Tx-Rx) antennas are modeled as random matrices which express nonhomogeneous environments. Then a knowledge-aided model which makes these random matrices share a prior covariance matrix structure is built to simulate the characteristics of clutter and noise in nonhomogeneous environments. On this basis, we design a new knowledge-aided Wald (KA-Wald) detector. Simulation results show that the proposed detector possesses a better detection performance compared with the traditional Wald detector. And relative to the knowledge-aided generalized likelihood ratio test (KA-GLRT) detector, the proposed KA-Wald detector has a similar detection performance but a higher efficiency.