TY - GEN
T1 - Research on Radar Emitter Signal Classification Technology based on SqueezeNet Lightweight Network
AU - Jiang, Hu
AU - Wang, Wei
AU - Wu, Juan
AU - Chen, Xi
AU - Han, Ruoyu
AU - Zhang, Zhiyong
AU - Shi, Zhan
AU - Li, Pengfei
N1 - Publisher Copyright:
© 2023 SPIE.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
KW - Missile-borne countermeasure system
KW - Radar emitter signals
KW - SqueezeNet lightweight network
KW - Time-frequency analysis
UR - http://www.scopus.com/inward/record.url?scp=85176233250&partnerID=8YFLogxK
U2 - 10.1117/12.3005021
DO - 10.1117/12.3005021
M3 - Conference contribution
AN - SCOPUS:85176233250
T3 - Proceedings of SPIE - The International Society for Optical Engineering
BT - Third International Conference on Signal Image Processing and Communication, ICSIPC 2023
A2 - Wang, Gang
A2 - Chen, Lei
PB - SPIE
T2 - 3rd International Conference on Signal Image Processing and Communication, ICSIPC 2023
Y2 - 26 May 2023 through 28 May 2023
ER -