论文部分内容阅读
在非均匀杂波背景下,传统的恒虚警检测算法,比如CA-CFAR,所选择的参考单元与待检测单元往往无法满足独立同分布的条件,导致背景杂波功率水平的估计值存在偏差,使得检测性能降低。针对上述问题,文中提出了一种基于雷达知识库的知识辅助恒虚警检测算法。首先,利用雷达环境知识来构建动态的雷达知识库;然后,利用雷达知识库中的先验信息来辅助参考单元的选择,提高背景杂波功率水平的估计准确性,从而降低非均匀背景带来的影响;最后,利用线性调频连续波雷达采集的实测数据对该算法性能进行了验证。结果表明:在非均匀杂波环境下,基于雷达知识库的知识辅助恒虚警检测算法比传统算法有更好的检测性能。
In the background of non-uniform clutter, the traditional constant false alarm detection algorithm, such as CA-CFAR, often fails to satisfy independent and identically distributed conditions for the selected reference unit and the unit to be detected, resulting in a bias in the estimation of the background clutter power level , Making the detection performance decreases. In view of the above problems, this paper proposes a knowledge-based constant false alarm detection algorithm based on radar knowledge base. First of all, using the knowledge of radar environment to build a dynamic radar knowledge base, and then using the prior knowledge in the radar knowledge base to assist the selection of reference cells and improve the estimation accuracy of the background clutter power level, Finally, the performance of this algorithm is verified by using the measured data acquired by LFM radar. The results show that under non-uniform clutter, knowledge-based CFAR detection algorithm based on radar knowledge base has better detection performance than traditional algorithms.