基于道路信息的知识辅助空时自适应处理

主波束中的车辆回波信号会污染空时自适应处理(STAP)的训练样本，导致空时自适应处理时的目标自相消，引起漏警。针对这一问题，该文提出一种基于道路信息的知识辅助(KA)空时自适应处理方法。该方法首先根据主波束中道路相对于雷达的位置估计道路上车辆相对于雷达的径向速度，然后得到可能含有主波束车辆回波信号的距离-多普勒单元，接着根据训练样本与杂波导向矢量和主波束导向矢量的匹配程度判断这些训练样本是否包含主波束车辆回波信号，最后在进行空时自适应处理估计杂波协方差矩阵时剔除被主波束车辆回波信号污染的训练样本。理论分析及实验结果表明该方法可以提高道路密集环境中空时自适应处理的信杂噪比输出，改善空时自适应处理雷达的性能。

A Knowledge Aided Space Time Adaptive Processing Based on Road Network Data

The echo of the vehicle from the main lobe may contaminate the training samples of Space Time Adaptive Processing (STAP), which results in target self nulling effect, and therefore degrades the probability of detection. To mitigate this problem, this paper proposes a Knowledge Aided (KA) STAP which is based on the road network data to select the training samples. This study firstly estimates the radial velocity of vehicle to the radar; then the range-Doppler cells which may contain vehicle echo are obtained according to the velocity; in the following, this study distinguish whether the training samples contain vehicle echo according to the matching degree of the training samples with the steering vector of the main lobe and the clutter; finally, the samples containing vehicle echo are discarded when the covariance matrix for the STAP is estimated. The theory analysis and experimental results illustrate that the proposed method advances the output of signal to clutter plus noise ratio, and improves the performance of STAP in the road network environments.