Radar signal modulation identification method and device, electronic equipment and storage medium

By introducing composite deformable convolution and Mamba's multi-branch multi-view feature extraction network (DCMNet) in radar signal modulation recognition, the problem of radar signal modulation recognition under complex electromagnetic environment and low signal-to-noise ratio is solved, and the recognition effect of high accuracy and robustness is achieved.

CN119716744AActive Publication Date: 2025-03-28YANTAI UNIV
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Patent Information

Application Number
CN202510212831.3
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-02-26
Publication Date
2025-03-28
Estimated Expiration
2045-02-26

AI Technical Summary

Technical Problem

Under complex electromagnetic environments and low signal-to-noise ratio conditions, traditional radar signal modulation and identification methods are difficult to effectively identify multiple modulated signals, and the calculation complexity is high.

Method used

A multi-branch multi-view feature extraction network (DCMNet) based on composite deformable convolution and Mamba is proposed. By inverting the deformable convolution, Mamba and cross-gated gated feature fusion mechanisms, a multi-branch multi-view feature extraction network is built to improve the modulation and recognition performance of radar signals.

Benefits of technology

DCMNet can effectively identify multiple radar signal modulation modes in complex electromagnetic environments, improve the accuracy and robustness of recognition, and operate efficiently with limited hardware resources.

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Abstract

The invention discloses a radar signal modulation identification method and device, electronic equipment and a storage medium, and belongs to the technical field of radar signal modulation identification, and the method comprises the following steps: 1, obtaining a radar signal, and generating a plurality of radar modulation signals; step 2, performing improved multiple synchronous compression transformation IMSST time-frequency analysis processing on the radar modulation signal, converting the radar modulation signal into a time-frequency image and forming a data set; step 3, a multi-branch multi-view feature extraction network DCMNet based on inverted deformable convolution IDC, Mama and cross-gated feature fusion CGFF is built; and step 4, training the multi-branch multi-view feature extraction network DCMNet by using the data set to obtain a radar signal modulation recognition model. According to the method, the modulation identification of the radar signal is realized, the modulation identification performance of the radar signal is improved, and the problem that various modulation signals are difficult to distinguish in a complex electromagnetic environment is solved.
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