小波神经网络的毫米波雷达目标一维距离像识别

将小波变换和反向传播神经网络理论结合，设计一种小波神经网络结构。由于小波变换在时间和频率空间所具有良好的定位特性，使小波神经网络可对输入输出数据进行多分辨的学习训练。介绍神经网络的数学框架和该网络的学习算法。根据毫米法频率步进雷达目标一维距离像所给出的信息，将所提出的小波神经网络用于3种实际雷达目标的识别。实验结果表明，小波神经网络收敛速度快、识别率高。

The Target Profile Identification of Step Frequency MMW Radar Based on Wavelet Neural Network

By integrating wavelet transform with feed forward neural network, a new adaptive wavelet function neural network is proposed. The good localization characteristics of wavelet functions in both time and frequency space allow hierarchical multi resolution learning of input output data mapping. The wavelet shapes are adaptively computed to minimize an energy function for a specific application of radar targets. The mathematical frame of the neural network is introduced and error back propagation algorithm is used. The procedure of using wavelet neural network for identification is described in detail. Based on the target specific information offered by the range profiles of step frequency MMW radar targets, the wavelet neural network is applied to recognition of three kinds of practical radar targets. Experiment results indicate that the wavelet neural network has faster convergence speed and higher correct recognition rate.