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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。
This paper introduces a radar signal sorting system based on self-organizing neural network. Probabilistic neural network realizes Bayesian sorting by calculating the joint probability density of input signal vector. Compared with the traditional signal sorting algorithm, And resource utilization has significantly improved. This parallel neural network computing architecture is also well suited for VLSI implementations. This article also introduced the system in complex radar signal simulation environment of the sorting test.