摘要
为解决雷达信号参数重叠导致雷达工作状态识别率下降的问题,将反向传播(backpropagation,BP)神经网络与AdaBoost算法相结合,生成BP-AdaBoost模型,对多种雷达特征参数进行分类,并对机载多功能相控阵雷达的6种不同的工作状态进行识别验证。仿真结果表明,与其他传统方法相比,BP-AdaBoost模型显著改善了识别过程,从而提高了识别精度。
To solve the problem that the recognition rate of radar working states is decreased due to the overlap of radar signal parameters,a back propagation(BP)neural network is combined with the AdaBoost algorithm to generate a BP-AdaBoost model,which can classify multiple radar feature parameters.Six different working states recognition of airborne multifunctional phased array radar is verified.The simulation results show that compared with other traditional methods,the BP-AdaBoost model significantly improves the recognition process and incereases the recognition accuracy.
作者
秦涛
陆满君
张文旭
胡建波
QIN Tao;LU Manjun;ZHANG Wenxu;HU Jianbo(School of Information and Communication Engineering,Harbin Engineering University,Harbin 150001,Heilongjiang,China;Shanghai Radio Equipment Research Institute,Shanghai 201109,China;No.91411 Unit of Chinese People’s Liberation Army,Dalian 116041,Liaoning,China)
出处
《制导与引信》
2022年第1期17-22,共6页
Guidance & Fuze
基金
黑龙江省自然科学基金(LH2020F020)。
