基于Scat-LSTM的小样本HRRP识别方法  

Method for small sample HRRP recognition based on Scat-LSTM

作　　者：程巍轶 张红敏[2] 高暄皓 CHENG Weiyi;ZHANG Hongmin;GAO Xuanhao(Unit 91911 of PLA,Sanya 572000,China;School of Data and Target Engineering,Information Engineering University,Zhengzhou 450001,China;Unit 61516 of PLA,Beijing 100071,China)

机构地区：[1]91911部队,海南三亚572000 [2]信息工程大学数据与目标学院,河南郑州450001 [3]61516部队,北京100071

出　　处：《信息对抗技术》2024年第5期51-61,共11页Information Countermeasure Technology

摘　　要：为提高小样本情况下的高分辨距离像(HRRP)目标识别精度,提出了一种基于小波散射变换的HRRP目标识别算法Scat-LSTM。首先,对原始信号进行小波散射变换,得到小波散射系数矩阵;然后,将该特征矩阵输入深度神经网络中进行训练和识别。实验结果表明,在样本量充足的情况下,相比于直接使用原始信号作为输入的方法,Scat-LSTM平均识别率提升了4%,并且在训练样本量极少的情况下,也能取得比其他算法更好的识别率。To improve the accuracy of high resolution range profile(HRRP)target recognition under small sample conditions,a HRRP target recognition algorithm Scat-LSTM based on wavelet scattering transformation was proposed.Firstly,the original signal was subjected to wavelet scattering transformation to obtain the wavelet scattering coefficient matrix.Then,this feature matrix was input into a deep neural network for training and recognition.The experimental results indicate that,given a sufficient sample size,the average recognition rate of Scat-LSTM has improved by 4%compared to methods that directly use raw signals as input.Moreover,even with extremely limited training samples,it can achieve better recognition rates than other algorithms.

关 键 词：雷达目标识别 高分辨距离像 小波散射变换 深度神经网络 

分 类 号：TN957.51[电子电信—信号与信息处理]