针对非均匀干扰环境中分布式多输入多输出(MIMO)雷达的距离扩展运动目标检测问题进行了研究。由于分布式MIMO雷达的干扰(杂波和噪声)是非均匀的, 无法获得训练数据来估计干扰的协方差矩阵, 为此提出了一种新的知识辅助(KA)干扰模型, 它和每个发射—接收(Tx-Rx)对相对应的干扰协方差矩阵具有服从复逆Wishart分布的矩阵结构, 但具有不同的功率电平。基于该模型提出了一种无训练数据的距离扩展目标知识辅助—广义似然比检测器(KA-GLRT)。仿真结果表明, 在非均匀干扰环境中, 所提出的KA-GLRT检测器的性能明显优于传统的有训练数据的协方差矩阵类检测器。

The problem of distributed MIMO radar in range-spread moving target detection under non-homogeneous environment is studied. Due to the fact that the disturbances (clutters and noises) on distributed MIMO radar are non-homogeneous, there is little homogeneous training data to estimate the disturbance covariance matrices. A new knowledge-aided model is proposed to depict the non-homogeneous characteristics of the disturbance in distributed MIMO radar.Specifically, the disturbance covariance matrices corresponding to different Transmit-Receive (Tx-Rx) pairs are modeled to obey the complex inverse Wishart distribution, but have different power levels.Based on the above prior knowledge, a novel Knowledge-Aided Generalized Likelihood Ratio Test (KA-GLRT) range-spread target detector is proposed for distributed MIMO radar without training data. Simulation results show that: compared with conventional covariance matrix-based (with training data) detectors, the proposed KA-GLRT detector has better detection performance in non-homogeneous environment.