Radar Emitter Recognition Based on Spiking Neural Networks

被引:2
作者
Luo, Zhenghao [1 ]
Wang, Xingdong [1 ]
Yuan, Shuo [1 ]
Liu, Zhangmeng [1 ]
机构
[1] Natl Univ Def Technol, Coll Elect Sci & Technol, Changsha 410073, Peoples R China
基金
中国国家自然科学基金;
关键词
radar signal processing; spiking neural networks; radar emitter recognition; electronic warfare; CLASSIFICATION;
D O I
10.3390/rs16142680
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Efficient and effective radar emitter recognition is critical for electronic support measurement (ESM) systems. However, in complex electromagnetic environments, intercepted pulse trains generally contain substantial data noise, including spurious and missing pulses. Currently, radar emitter recognition methods utilizing traditional artificial neural networks (ANNs) like CNNs and RNNs are susceptible to data noise and require intensive computations, posing challenges to meeting the performance demands of modern ESM systems. Spiking neural networks (SNNs) exhibit stronger representational capabilities compared to traditional ANNs due to the temporal dynamics of spiking neurons and richer information encoded in precise spike timing. Furthermore, SNNs achieve higher computational efficiency by performing event-driven sparse addition calculations. In this paper, a lightweight spiking neural network is proposed by combining direct coding, leaky integrate-and-fire (LIF) neurons, and surrogate gradients to recognize radar emitters. Additionally, an improved SNN for radar emitter recognition is proposed, leveraging the local timing structure of pulses to enhance adaptability to data noise. Simulation results demonstrate the superior performance of the proposed method over existing methods.
引用
收藏
页数:22
相关论文
共 40 条
[1]   An intelligent radar signal classification and deinterleaving method with unified residual recurrent neural network [J].
Al-Malahi, Abdulrahman ;
Farhan, Abubaker ;
Feng, HanCong ;
Almaqtari, Omar ;
Tang, Bin .
IET RADAR SONAR AND NAVIGATION, 2023, 17 (08) :1259-1276
[2]   A surrogate gradient spiking baseline for speech command recognition [J].
Bittar, Alexandre ;
Garner, Philip N. .
FRONTIERS IN NEUROSCIENCE, 2022, 16
[3]   Recognition and Estimation for Frequency-Modulated Continuous-Wave Radars in Unknown and Complex Spectrum Environments [J].
Chen, Kuiyu ;
Zhang, Jingyi ;
Chen, Si ;
Zhang, Shuning ;
Zhao, Huichang .
IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS, 2023, 59 (05) :6098-6111
[4]   Radar emitter classification for large data set based on weighted-xgboost [J].
Chen, Wenbin ;
Fu, Kun ;
Zuo, Jiawei ;
Zheng, Xinwei ;
Huang, Tinglei ;
Ren, Wenjuan .
IET RADAR SONAR AND NAVIGATION, 2017, 11 (08) :1203-1207
[5]   A Knowledge Graph-Driven CNN for Radar Emitter Identification [J].
Chen, Yingchao ;
Li, Peng ;
Yan, Erxing ;
Jing, Zehuan ;
Liu, Gaogao ;
Wang, Zhao .
REMOTE SENSING, 2023, 15 (13)
[6]   A Neuromorphic Processing System With Spike-Driven SNN Processor for Wearable ECG Classification [J].
Chu, Haoming ;
Yan, Yulong ;
Gan, Leijing ;
Jia, Hao ;
Qian, Liyu ;
Huan, Yuxiang ;
Zheng, Lirong ;
Zou, Zhuo .
IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS, 2022, 16 (04) :511-523
[7]  
Dash Dillip, 2021, Advances in Automation, Signal Processing, Instrumentation, and Control. Select Proceedings of i-CASIC 2020. Lecture Notes in Electrical Engineering (LNEE 700), P2655, DOI 10.1007/978-981-15-8221-9_248
[8]  
Gong Liangliang, 2010, Proceedings of the 2010 2nd International Conference on Signal Processing Systems (ICSPS 2010), P153, DOI 10.1109/ICSPS.2010.5555410
[9]   Comparing SNNs and RNNs on neuromorphic vision datasets: Similarities and differences [J].
He, Weihua ;
Wu, YuJie ;
Deng, Lei ;
Li, Guoqi ;
Wang, Haoyu ;
Tian, Yang ;
Ding, Wei ;
Wang, Wenhui ;
Xie, Yuan .
NEURAL NETWORKS, 2020, 132 :108-120
[10]   Spiking Neural Networks for LPI Radar Waveform Recognition with Neuromorphic Computing [J].
Henderson, Alex ;
Harbour, Steven ;
Yakopcic, Chris ;
Taha, Tarek ;
Brown, David ;
Tieman, Justin ;
Hall, Garrett .
2023 IEEE RADAR CONFERENCE, RADARCONF23, 2023,