基于Scat-LSTM的小样本HRRP识别方法

Method for small sample HRRP recognition based on Scat-LSTM

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.