摘要
为了进一步提高雷达的探测性能,设计了线性调频–二相码(LFM-M)混合调制脉冲压缩信号。采用分类比较的方法,研究了反向传播网络、Elman网络和径向基函数(RBF)网络等3种典型神经网络在其脉冲压缩中的应用,设计了网络的结构,分析了网络的算法。通过仿真和对脉冲压缩输出性能的研究得出,采用RBF神经网络对LFM-M码信号进行脉冲压缩,网络具有较快的收敛速度和较好的数值稳定性,可获得60 d B左右的输出主旁瓣比。
Linear Frequency Modulation(LFM)-M code hybrid modulation pulse compression signal is designed in order to further improve the detection performance of radar. Classified comparison is used to research the application of such three classic neural networks as Back Propagation(BP) network, Elman network, and Radial-Based Function(RBF) network to pulse compression. Three network structures are designed, and their corresponding network algorithms are analyzed. Through the research on simulation and pulse compression output performance, it is indicated that a faster convergence rate and better numerical stability can be obtained in the pulse compression of LFM-M code signal by adopting RBF neural network, with the output main-to-side lobe of about 60 dB.
出处
《太赫兹科学与电子信息学报》
2015年第1期118-121 129,129,共5页
Journal of Terahertz Science and Electronic Information Technology
关键词
线性调频–二相码(LFM-M)信号
脉冲压缩
反向传播网络
径向基函数网络
主旁瓣比
Linear Frequency Modulation(LFM)-M code signal
pulse compression
Back Propagation neural network
Radial-Based Function neural network
main-to-side lobe ratio
