TY - GEN
T1 - Recognition of radar in-pulse modulation based on radar de-noising Linknet
AU - Wang, Chenkai
AU - Kuang, Lei
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Intra-pulse modulation pattern recognition of radar signals is an important aspect of modern electronic reconnaissance warfare. For radar intra-pulse modulated signals with low signal-to-noise ratio (SNR), traditional machine learning and deep learning networks do not preprocess the signals properly, resulting in low recognition accuracy. To solve this problem, this paper proposes a semantic segmentation network-based method to improve the recognition rate of modulation patterns by preprocessing the semantics of signal time-frequency maps. The experimental results show that the method can effectively recognize 12 radar modulation patterns at low SNR, and the pattern recognition accuracy is as high as 98.6% for signals with SNR of -10db.
AB - Intra-pulse modulation pattern recognition of radar signals is an important aspect of modern electronic reconnaissance warfare. For radar intra-pulse modulated signals with low signal-to-noise ratio (SNR), traditional machine learning and deep learning networks do not preprocess the signals properly, resulting in low recognition accuracy. To solve this problem, this paper proposes a semantic segmentation network-based method to improve the recognition rate of modulation patterns by preprocessing the semantics of signal time-frequency maps. The experimental results show that the method can effectively recognize 12 radar modulation patterns at low SNR, and the pattern recognition accuracy is as high as 98.6% for signals with SNR of -10db.
KW - de-noising
KW - high recognition rate
KW - intra-pulse modulation recognition
KW - low SNR
KW - semantic segmentation
UR - https://www.scopus.com/pages/publications/85143354060
U2 - 10.1109/CEI57409.2022.9950156
DO - 10.1109/CEI57409.2022.9950156
M3 - 会议稿件
AN - SCOPUS:85143354060
T3 - 2022 2nd International Conference on Computer Science, Electronic Information Engineering and Intelligent Control Technology, CEI 2022
SP - 233
EP - 236
BT - 2022 2nd International Conference on Computer Science, Electronic Information Engineering and Intelligent Control Technology, CEI 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2nd International Conference on Computer Science, Electronic Information Engineering and Intelligent Control Technology, CEI 2022
Y2 - 23 September 2022 through 25 September 2022
ER -