A minimum description length constant false alarm algorithm based on weighted likelihood sparse regularization

Ground reconnaissance radar has the characteristics of large detection range, all-weather, all-day and so on, and has become the mainstream reconnaissance equipment at domestic and international. In order to adapt to the more complex working environment, the constant false alarm detection algorithm is widely used in the automatic detection of radar signals. The research on constant false alarm algorithm of ground radar is of great significance to improve the detection performance and environment adaptability of ground radar. Aiming at the problem of poor detection performance of the minimum description length constant false alarm (MDLCFAR) algorithm in multi-target environment, a minimum description length constant false alarm algorithm based on weighted likelihood sparse regularization (WLSRMDL-CFAR) is proposed. The non-convex regularization term is used to regularize the outliers, the indicator function is introduced to improve the maximum likelihood estimation process, and the robustness of outlier vector determination is improved based on the median idea. The algorithm solves the problem that the detection threshold is greatly raised, suppresses the “target masking effect” and proves the detection performance in multi-target environment.