海杂波背景下的PSO-RBF弱目标检测

海杂波背景下的目标检测是海面雷达信号处理的重要组成部分。海杂波中弱目标的检测，传统方法是基于海杂波统计特性，但是统计特性并不能很好地反映海杂波的内在动力学特性，因此检测效果很不理想。本文根据海杂波的混沌特性，对其进行了相构空间重构，并将粒子群算法算法(PSO)应用到径向基函数(RBF)神经网络核函数参数的优化学习中，利用加拿大McMaster大学采用IPIX雷达在Dartmouth地区海域实测带有目标的海杂波数据对此方法进行验证。结果表明，在混沌海杂波背景下PSO-RBF小目标检测法具有良好的预测性，相比于一般的径向基神经网络，改进算法不仅收敛速度快，且误差精度高。

PSO-RBF small target detection based on sea clutter

Target detection in the background of sea clutter is an important part of sea surface radar signal processing. The detection of weak targets in sea clutter is based on the statistical characteristics of sea clutter, but the statistical persistence 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 phase space of the sea clutter, and particle swarm optimization (PSO) is applied to radial basis function(RBF) neural network kernel function parameters. In the optimization study, this method is validated by using IPIX radar to measure sea clutter with target in Dartmouth area. The results show that: PSO-RBF small target detection method has good predictability in the background of chaotic sea clutter. Compared with the general radial basis neural network, the improved algorithm not only has fast convergence speed but also has high recognition rate.