基于神经网络的多功能雷达行为辨识方法

针对多功能雷达行为状态复杂多变、难以识别的问题,构建了多功能雷达行为数据集,提出了一种基于神经网络的雷达行为辨识方法。首先对数据进行预处理,提取多功能雷达的参数特征与行为状态特征,并建立两者间的映射关系。然后通过基于贝叶斯准则的变化点检测算法对原始雷达信号脉冲序列进行分割,补齐有缺失的特征参数,构造完整的可用于训练的脉冲数组样本。最后通过数据推理丰富数据库,为数据驱动的智能识别方法提供可靠的数据准备,增强神经网络的泛化能力。针对处理后的雷达行为数据集的特点,设计BP神经网络进行训练与测试。仿真实验结果表明:训练完成的网络模型在识别过程中一定程度上克服了噪声变量等干扰的影响,正确率可以达到89%。

A Multifunctional Radar Behavior Identification Method Based on Neural Network

Aimed at the problem that the multifunctional radar behavior state is complicated and difficult to identify, a multifunctional radar behavior data set is constructed, and a method of radar behavior identification based on neural network is proposed. Firstly, the data are preprocessed to extract the parameter characteristics and behavior state characteristics of the multi function radar from and to establish a mapping relationship between them. Then, the original radar signal pulse sequence is segmented by the Bayesian rule based on the change point detection algorithm, and the missing characteristic parameters are supplemented to construct a complete pulse array sample to train. Finally, the database is enriched by data reasoning, providing reliable data preparation for data driven intelligent identification method and enhancing the generalization ability of neural network. BP neural network is designed to train and test the characteristics of the processed radar behavior data set. The simulation results show that the trained network model overcomes the influence of noise variables and other disturbances to some extent, and the accuracy can reach 89%.