不需要训练数据的分布式MIMO雷达距离扩展目标检测器

A Range-spread Target Detector for Distributed MIMO Radar without Training Data

在非均匀杂波环境中,研究了高分辨率分布式多输入多输出(MIMO)雷达的距离扩展目标检测问题。由于分布式MIMO雷达观测到的杂波是非均匀的,无法获得足够的独立同分布的均匀训练数据来估计检测单元的杂波协方差矩阵。采用复逆Wishart分布对杂波协方差矩阵建模,基于该杂波模型设计了一种不需要训练数据的分布式MIMO雷达距离扩展目标广义似然比检测器。数值仿真结果表明:在非均匀杂波环境中,所设计的检测器的性能比用训练数据的协方差矩阵类检测器有明显的改进。

The problem of range-spread target detection for high resolution distributed multiple-input multiple-output(MIMO) radar in non-homogeneous clutters is studied. Due to the fact that the clutters in distributed MIMO radar are non-homogeneous,there is little homogeneous training data to estimate the disturbance covariance matrices. A new model is proposed to depict the non-homogeneous characteristics of the clutters in distributed MIMO radar. Namely,the clutter covariance matrices are modeled as obeying the inverse complex Wishart distribution with different power levels. Based on this clutter model,a new knowledge-aided generalized likelihood ratio test range-spread target detector for distributed MIMO radar without training data is proposed. Numerical simulations show that detection performances of the proposed detector have obvious improvements compared with the covariance matrixbased detectors with training data.