基于DTCWT-VAE的弹道中段目标RCS识别

doi:10.12305/j.issn.1001-506X.2024.07.10

Abstract

Abstract:

Aiming at the problem that the radar signal of ballistic target is easily affected by the environment and the target recognition accuracy is low, a radar cross section (RCS) recognition method of ballistic target based on dual-tree complex wavelet transform (DTCWT) and variational autoencoder (VAE) is proposed. Firstly, the dynamic datas of ballistic target RCS are preprocessed by DTCWT. Then, the hidden variable features of target are extracted by VAE. Finally, the support vector machine (SVM) classifier is used to identify the data. Experimental results show that compared with the existing methods, the proposed method has higher recognition probability and better robustness.

Key words: ballistic target, target recognition, radar cross section (RCS), dual-tree complex wavelet transform (DTCWT), variational autoencoder (VAE)

CLC Number:

TN957

Caiyun WANG, Huiwen ZHANG, Jianing WANG, Yida WU, Yun CHANG. Ballistic midcourse target RCS recognition based on DTCWT-VAE[J]. Systems Engineering and Electronics, 2024, 46(7): 2269-2275.

Figures/Tables 7

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类别	自旋频率/Hz	进动频率/Hz	进动角/(°)	章动频率/Hz	章动角/(°)	翻滚频率/Hz	翻滚角/(°)
弹头	1.5	2∶0.5∶4	3∶0.375∶4.5	—	—	—	—
诱饵1	3	2.5∶0.5∶4.5	5∶0.375∶6.5	1	2	—	—
诱饵2	2	1.5∶0.5∶3.5	4∶0.375∶5.5	0.5	1	—	—
碎片	—	—	—	—	—	2∶1∶6	70∶5∶90

方法名称	准确率				平均准确率
方法名称	弹头	诱饵1	诱饵2	碎片	平均准确率
11种统计特征-PCA^[28]-AdaBoostM2	79.15	90.06	85.55	95.96	87.68
11种统计特征-PCA^[28]-SVM	84.72	91.39	86.31	96.30	89.68
9种特征^[35]-AdaBoostM2	84.55	80.42	86.67	93.88	86.43
9种特征^[35]-SVM	79.72	90.86	88.19	94.46	88.31
本文方法	92.65	95.96	97.02	99.70	96.33

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Ballistic midcourse target RCS recognition based on DTCWT-VAE

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