基于自组织神经网络的雷达信号分选

Radar signal Classification Based on a self-organized probabinistic Neural Network

本文介绍了一种基于自组织神经网络的雷达信号分选系统，概率神经网络通过计算输入信号矢量的联合概率密度实现贝叶斯分选，它与传统的信号分选算法相比在分选精度和资源利用率上有显著的提高。这种并行的神经网络计算结构也很适合于ＶＬＳＩ实现。本文还介绍了此系统在复杂雷达信号环境下的仿真分选试验。

Based on a self-organized probabilistic neural network(PNN) paradim,a parallel network can be used to sort data parameters into classes with high sorting accuracy and fragmentation.The PNN implements the statistical Bayesian strategy by computing a joint probability density over all input parameters to match a group of candidate data classes,the sorting is accomplished by assigning the inputs to most likely group with highest probability density estimate.Then the prospect of applying the self-organized PNN to ESM pulse data sorting will be shown,and a system including self-organized PNN and pulse repeating interval sorting will be discussed under the limited conditions of the sorting after lots of emulated experiments.