一种基于AR模型的矩阵CFAR检测器

矩阵恒虚警（Constant False Alarm Rate，CFAR）检测器是根据信息几何理论利用检测单元相关矩阵与参考单元矩阵黎曼均值间的测地线距离作为检测统计量进行检测.本文根据尤尔-沃克方程中相关矩阵与自回归（Auto-Regressive，AR）谱之间的一一对应关系，将矩阵CFAR检测器中两矩阵间的距离转化为两AR谱之间的距离，提出了一种基于AR模型的矩阵CFAR检测器，该检测器能利用分辨率更高的AR模型进行谱估计，并将功率谱整体进行检测，能充分利用回波的多普勒信息，非常适用于频域扩展目标的检测，通过利用具有频域扩展特性的尾流实测数据对该方法和传统方法的检测性能进行比较分析，验证了本文所提检测器的有效性.

A Matrix CFAR Detector Based on AR Model

The matrix constant false alarm rate (CFAR)detector uses the geodesic distance between the correlation matrix of the test cell and matrix Riemannian mean of the reference cells to detect target according to the information geometry theory.Based on the one-to-one corresponding relations between correlation matrix and AR(Auto-Regressive) spectrum,an improved matrix CFAR detector based on AR model is proposed with the distance between two matrices changed by the distance between two AR spetrums.The proposed detector is of higher frequency resolution and detects the spectrum of wake vortex in whole,fully taking advantage of the Doppler information of wake vortex.The detection performance of the proposed method and traditional methods is compared by using actual measured wake vortex data.