Knowledge-aided Space Time Adaptive Processing for Airborne Radar in Heterogeneous Environments

Airborne radar can be used to surveillance wide area and monitor interesting targets, which makes it a good choice for military and civil applications. However, the ground clutter is spread over a region in Doppler due to the movement of the platform, and potential slowly moving target may be obscured by the heavy clutter. To solve this problem, a knowledge-aided Space-time Adaptive Processing (KA-STAP) algorithm is proposed. In particular, the prior knowledge of clutter range-variance property as well as the information extracted from the radar data have been fully utilized. Firstly, the blocked sample selection strategy in range is utilized to decrease the range-variance effect. Then, low threshold CFAR strategy is exploited to choose proper samples in the secondary data, which is quite useful to eliminate the potential targets or jammings. By exploiting this prior knowledge information, the accuracy of the estimated covariance matrix can be well improved. Therefore, the clutter suppression performance can be enhanced in heterogeneous environment. Finally, the performance of different clutter suppression methods are analyzed by resorting to airborne experimental results, which further confirms the effectiveness of the proposed algorithm.