PSO-RBF Small Target Detection in Sea Clutter Background

Abstract

Target detection in the background of sea clutter is an important part of sea surface radar signal processing. The traditional detection of weak targets in sea clutter is based on the statistical characteristics of sea clutter, which does not reflect the intrinsic dynamics of sea clutter. Therefore, the detection results are not ideal. Based on the chaotic characteristics of sea clutter, this dissertation reconstructs the space structure of the sea clutter and proposes an improved particle swarm optimization (PSO) algorithm based on adaptive time-varying weights and local search operators. This method was applied to the optimization learning of the parameters of the radial basis function (RBF) neural network kernel function. The method was validated by using McIX University in Canada to measure the sea clutter data with the target in the Dartmouth area using IPIX radar. The results showed that the PSO-RBF algorithm in the background of chaotic sea clutter has good predictability. Compared with the general radial basis neural network, the improved algorithm not only has fast convergence speed but also has high error accuracy.