一种Chirplet神经网络自动目标识别算法  

A Chirplet neural network for automatic target recognition

作　　者：李怡霏 郭尊华[1] LI Yifei;GUO Zunhua(School of Mechanical,Electrical&Information Engineering,Shandong University(Weihai),Weihai 264209,Shandong,China)

机构地区：[1]山东大学(威海)机电与信息工程学院,山东威海264209

出　　处：《山东大学学报（工学版）》2020年第3期8-14,共7页Journal of Shandong University（Engineering Science）

基　　金：国家自然科学基金资助项目(61401252)。

摘　　要：针对飞机目标的自动识别问题,提出一种联合特征提取与分类的Chirplet神经网络方法,实现一维高分辨率距离像的识别。Chirplet神经网络将Chirplet原子变换用于多层前馈神经网络结构的输入层,替换传统的激励函数对距离像序列进行特征提取;网络的分类部分由隐层和输出层组成。在训练过程中调整神经网络权值的同时,完成对Chirplet原子时频参数的自动调整,协调优化特征参数和分类器参数,使Chirplet神经网络同时实现特征提取和目标分类。对4类飞机目标的仿真测试结果表明,相比时频变换和Gabor原子网络等方法,具有四特征参数的Chirplet神经网络方法具有较高的识别率和抗噪性能。Aiming at automatic target recognition of aircrafts,a Chirplet neural network for joint feature extraction and target classification was proposed to realize recognition of one-dimensional high resolution range profiles.Based on the multilayer feedforward neural network structure,the Chirplet-atom transform was used to replace the conventional excitation function in the input layer for feature extraction,and the hidden layer and output layer constituted the classifier of the network.The network weights and the parameters of Chirplet-atom node were simultaneously adjusted and optimized to achieve joint feature extraction and target classification.The simulation results of the four types of aircrafts showed that the Chirplet neural network method with the four-feature-parameters had higher recognition rate and anti-noise performance than the time-frequency transformation and Gabor atoms network.

关 键 词：自动目标识别 高分辨率距离像 Chirplet神经网络 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]