A Passive Jamming Identification Method based on HRRP and SSM-Transformer

Jamming identification is an important part of the field of radar technology in the modern battlefield and is a prerequisite for the implementation of radar anti-jamming measures. Passive jamming creates multiple false targets by scattering radar transmitter signals to induce and deceive radar. Aiming at the problems of existing passive jamming recognition methods relying on manual feature extraction and insufficient utilization of timing features, a passive jamming recognition method based on SSM-Transformer is proposed. The method first characterises the timing-dependent information of HRRP sequences through the SSM module, and then extracts the deep timing-associated features by using the multi-head attention mechanism in the Transformer encoder. The experimental results show that the proposed method has a recognition accuracy of up to 98% when the JNR is not lower than 21dB, which has higher recognition accuracy and better generalisation performance compared with the existing methods.

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Publication Details

Published
2024-11-22
DOI
https://doi.org/10.1109/icsidp62679.2024.10868602
Primary Topic
Gait Recognition and Analysis
Type
article
Field-Weighted Citation Impact
0.00

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A Passive Jamming Identification Method based on HRRP and SSM-Transformer

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Gait Recognition and Analysis
article

A Passive Jamming Identification Method based on HRRP and SSM-Transformer

Jiaxiang Zhang, Zhennan Liang, Bo Wang, Tianjun Zhang, Quanhua Liu
article en

Abstract

Jamming identification is an important part of the field of radar technology in the modern battlefield and is a prerequisite for the implementation of radar anti-jamming measures. Passive jamming creates multiple false targets by scattering radar transmitter signals to induce and deceive radar. Aiming at the problems of existing passive jamming recognition methods relying on manual feature extraction and insufficient utilization of timing features, a passive jamming recognition method based on SSM-Transformer is proposed. The method first characterises the timing-dependent information of HRRP sequences through the SSM module, and then extracts the deep timing-associated features by using the multi-head attention mechanism in the Transformer encoder. The experimental results show that the proposed method has a recognition accuracy of up to 98% when the JNR is not lower than 21dB, which has higher recognition accuracy and better generalisation performance compared with the existing methods.

Beijing Institute of Technology (CN)
National Natural Science Foundation of China, Fundamental Research Funds for the Central Universities
Openalex Percentile: Top 76%
Gait Recognition and Analysis
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