A sparse probabilistic approach with chaotic artificial bee colony optimization for sea clutter soft computing

Abstract

A novel soft computing method of sea clutter based on sparse probabilistic learning frameworks with an optimizing approach is proposed, where a probabilistic dynamic computing method of electromagnetic signals by relevance vector machine (RVM) is developed with sensor parameters optimization using a novel chaotic artificial bee colony (CABC) algorithm. LS-SVM, WLS-SVM and ABC-RVM soft computing models of sea clutter are also developed as the comparative basis. The experimental results show that new optimizing method outperforms the basic ABC both in convergence speed and calculation precision, and then an efficient CABC-RVM approach for computing sea clutter is presented and confirmed through real sea clutter data. Furthermore, the performance of CABC-RVM is analyzed and compared to above sea clutter sensors and literature reported sea clutter sensors in detail. The research results show effectiveness of the proposed approach.