A Novel Clutter Suppression Algorithm for Low-Slow-Small Targets Detecting Based on Sparse Adaptive Filtering

Passive detection of low-slow-small(LSS) targets is easily interfered by direct signal and multipath clutter, and the traditional clutter suppression method has the contradiction between step size and convergence rate. In this paper, a frequency domain clutter suppression algorithm based on sparse adaptive filtering is proposed. The pulse compression operation between the error signal and the input reference signal is added to the cost function as a sparsity constraint, and the criterion for filter weight updating is improved to obtain a purer echo signal. At the same time, the step size and penalty factor are brought into the adaptive iteration process, and the input data is used to drive the adaptive changes of parameters such as step size. The proposed algorithm has a small amount of calculation, which improves the robustness to parameters such as step size, reduces the weight error of the filter and has a good clutter suppression performance.