Adaptive Regularisation for Radar Sea Clutter Signal Separation Using a Sparse-Based Method

摘要

Detection in the maritime domain requires the radar return from the target to be distinguishable from the background interference (sea clutter and noise). Sparse signal separation is a technique which has recently been applied to this problem with promising results. Achieving good separation of the target signal relies on two main factors: an appropriately chosen transform that encourages a sparse representation and the regularisation (or penalty) term in the optimisation. In this paper, we use the popular tuned Q wavelet transform to analyse the impact of the penalty term on the separation of targets from the background interference. An adaptive method is proposed for the penalty parameter selection which suppresses the interference fluctuations more uniformly over range and improves the discrimination of targets. The detection improvement is then demonstrated with a Monte-Carlo simulation.