Constant false alarm rate detector based on sparsity regularisation in multi-target interfering Weibull clutter

In this study, the authors propose a new constant false alarm rate detection algorithm in the non-homogeneous Weibull clutter caused by point-like targets interference. In their algorithm, sparsity regularisation is imposed on the target, which makes use of the target minority in detection background. On the basis of the regularised estimate of outliers, the indicator function is introduced to select the clean samples out of the background to estimate the distribution parameters, which further improves the robustness of the proposed detector. Simulation and experimental results verify the performance of the proposed detector which illustrates its superiority by making a comparison with the conventional detectors.