Research on radar emitter signal classification technology based on SqueezeNet lightweight network

Hu Jiang; Wei Wang; Juan Wu; Xi Chen; Ruoyu Han; Zhiyong Zhang; Zhan Shi; Pengfei Li

doi:10.1117/12.3005021

20 October 2023 Research on radar emitter signal classification technology based on SqueezeNet lightweight network

Hu Jiang, Wei Wang, Juan Wu, Xi Chen, Ruoyu Han, Zhiyong Zhang, Zhan Shi, Pengfei Li

Proceedings Volume 12916, Third International Conference on Signal Image Processing and Communication (ICSIPC 2023); 129160Z (2023) https://doi.org/10.1117/12.3005021
Event: Third International Conference on Signal Image Processing and Communication (ICSIPC 2023), 2023, Kunming, China

Abstract

With the rapid advancement of information technology, the modern battlefield is characterized by a highly complex electromagnetic environment. Radar radiation sources exhibit wide-ranging parameter variations and strong random characteristics, presenting formidable challenges to the signal selection of radar radiation sources in missile-borne countermeasure systems. This paper addresses the issue of reliable identification and selection of radar source signals by on-board countermeasures systems. Through the analysis of source signal characteristics, the Smooth Pseudo Wigner-Ville Distribution (SPWVD) method is employed for time-frequency analysis to extract the time-frequency features of the source signals. Furthermore, a lightweight network based on SqueezeNet is implemented to achieve high-precision source signal selection. The results demonstrate that, when the SNR of the source signals is greater than 0dB, the network model achieves a recognition accuracy above 94.59%. The selection accuracy is comparable to that of the Convolutional Neural Network (CNN), thereby meeting the requirements of on-board countermeasure systems for reliable selection of radar source signals. The analysis confirms that under low signal-to-noise ratio conditions, noise significantly affects the network's selection accuracy by impacting the time-frequency clarity of the modulation signals.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Hu Jiang, Wei Wang, Juan Wu, Xi Chen, Ruoyu Han, Zhiyong Zhang, Zhan Shi, and Pengfei Li "Research on radar emitter signal classification technology based on SqueezeNet lightweight network", Proc. SPIE 12916, Third International Conference on Signal Image Processing and Communication (ICSIPC 2023), 129160Z (20 October 2023); https://doi.org/10.1117/12.3005021

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KEYWORDS

Modulation

Signal to noise ratio

Time-frequency analysis

Radar signal processing

Education and training

Interference (communication)

Continuous wavelet transforms

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