基于Scat-LSTM的小样本HRRP识别方法
Method for small sample HRRP recognition based on Scat-LSTM
程巍轶 1张红敏 2高暄皓3
作者信息
- 1. 91911部队,海南三亚 572000;信息工程大学数据与目标学院,河南郑州 450001
- 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
- 折叠
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.
Abstract
To improve the accuracy of high resolution range profile(HRRP)target recogni-tion 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 recogni-tion 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Key words
radar target recognition/HRRP/wavelet scattering transform/deep neural networks引用本文复制引用
出版年
2024
NETL
NSTL
万方数据