Radar Emitter Signal Identification Via Distance Features

doi:10.16182/j.issn1004731x.joss.21-FZ0808

Abstract

Abstract: Aiming at the problem that traditional recognition methods of radar emitter signal have low accuracy in low signal to noise ratio (SNR) environment, and are usually suitable for only several specific radar signals, an identification approach of radar signal based on distance features is proposed. Several cluster centers are extracted via the k-means algorithm, and the Dynamic Time Warping (DTW) values between the radar signal and the cluster center are calculated respectively, which are combined as the input features of k-Nearest Neighbor (k-NN) algorithm. The simulation results show that when the SNR is 3 dB, the identification rate of the 6 classes of radar signals is 91%. Compared to the method based on wavelet ridge-frequency cascade-feature, the proposed method also shows better recognition performance.

Key words: radar emitter signals identification, cluster center, Dynamic Time Warping (DTW) method, k-nearest neighbor algorithm, distance features

CLC Number:

TP391.9

Huang Yingkun, Jin Weidong, Yan Kang, Zhu Jiehao. Radar Emitter Signal Identification Via Distance Features[J]. Journal of System Simulation, 2021, 33(12): 2959-2966.

References

[1] 候印鸣. 综合电子战[M]. 北京: 国防工业出版社, 2000: 1-32.
Hou Yinming.Synthetic EW[M]. Beijing: National Defense Industry Press, 2000: 1-32.
[2] Pang J, Lin Y, Xu X.The Improved Radial Source Recognition Algorithm Based on Fractal Theory and Neural Network Theory[J]. International Journal of Hybrid Information Technology (S2511-2104), 2014, 7(2): 397-402.
[3] Zhang X L, You W T, Guo Q, et al.Recognition Method Studies for Radar and Communication Signals based on Spectral Correlation[C]// International Symposium on Systems and Control in Aeronautics and Astronautics. Piscataway, USA: IEEE Press, 2010: 363-366.
[4] Guo Q, Nan P, Zhang X, et al.Recognition of Radar Emitter Signals Based on SVD and AF Main Ridge Slice[J]. Journal of Communications and Networks (S1229-2370), 2015, 17(5): 491-498.
[5] Li Y, Wang Y, Lin Y.Recognition of Radar Signals Modulation Based on Short Time Fourier Transform and Reduced Fractional Fourier Transform[J]. Journal of Information and Computational Science (S1548-7741), 2013, 10(16): 5171-5178.
[6] Konopko K, Grishin Y P, Jańczak D.Radar Signal Recognition based on Time-frequency Representations and Multidimensional Probability Density Function Estimator[C]// Signal Processing Symposium. Debe, Poland: IEEE Press, 2015: 1-6.
[7] 余志斌, 金炜东, 陈春霞. 基于小波脊频级联特征的雷达辐射源信号识别[J]. 西南交通大学学报, 2010, 45(2): 290-295.
Yu Zhibin, Jin Weidong, Chen Chunxia.Radar Emitter Signal Recognition based on WRFCCF[J]. Journal of Southwest Jiaotong University, 2010, 45(2): 290-295.
[8] Wang C, Wang J, Zhang X.Automatic Radar Waveform Recognition based on Time-frequency Analysis and Convolutional Neural Network[C]// IEEE International Conference on Acoustics, Speech and Signal Processing. New Orleans, USA: IEEE Press, 2017: 2437-2441.
[9] Zhang M, Diao M, Guo L.Convolutional Neural Networks for Automatic Cognitive Radio Waveform Recognition[J]. IEEE Access (S2169-3536), 2017, 5(6): 11074-11082.
[10] Zhou Z, Huang G, Chen H, et al.Automatic Radar Waveform Recognition Based on Deep Convolutional Denoising Auto-encoders[J]. Circuits Systems and Signal Processing (S0278-081X), 2018, 37(1): 1-15.
[11] 周志文, 黄高明, 高俊, 等. 一种深度学习的雷达辐射源识别算法[J]. 西安电子科技大学学报, 2017, 44(3): 85-90.
Zhou Zhiwen, Huang Gaoming, Gao Jun, et al.Radar Emitter Identification Algorithm based on Deep Learning[J]. Journal of Xidian University, 2017, 44(3): 85-90.
[12] Zhang M, Diao M, Gao L, et al.Neural Networks for Radar Waveform Recognition[J]. Symmetry (S2073-8994), 2017, 9(5): 75.
[13] Wan Y, Chen X L, Shi Y.Adaptive Cost Dynamic Time Warping Distance in Time Series Analysis for Classification[J]. Journal of Computational and Applied Mathematics (S0101-8205), 2017, 319(8): 514-520.
[14] Zhang Z, Tavenard R, Bailly A, et al.Dynamic Time Warping Under Limited Warping Path Length[J]. Information Sciences (S0020-0255), 2017, 393(7): 91-107.
[15] 余志斌. 基于脉内特征的雷达辐射源信号识别研究[D]. 成都: 西南交通大学, 2010.
Yu Zhibin.Study on Radar Emitter Signal Identification based on Intra-pulse Features[D]. Chengdu: Southwest Jiaotong University, 2010.
[16] Tsai C F, Lin W Y, Hong Z F, et al.Distance-based Features in Pattern Classification[J]. Eurasip Journal on Advances in Signal Processing (S1687-6172), 2011(1): 1-11.
[17] Guo C, Zhou Y, Ping Y, et al.A Distance Sum-based Hybrid Method for Intrusion Detection[J]. Applied Intelligence (S0924-669X), 2014, 40(1): 178-188.
[18] Lin W C, Ke S W, Tsai C F.CANN: An Intrusion Detection System Based on Combining Cluster Centers and Nearest Neighbors[J]. Knowledge-Based Systems (S0950-7051), 2015, 78(1): 13-21.
[19] Kate R J.Using Dynamic Time Warping Distances as Features for Improved Time Series Classification[J]. Data Mining and Knowledge Discovery (S1384-5810), 2015, 30(2): 1-30.
[20] Hall P, Pham T.Optimal Properties of Centroid-based Classifiers for Very High-dimensional Data[J]. Annals of Statistics (S0090-5364), 2010, 38(2): 1071-1093.
[21] Mori U, Mendiburu A, Keogh E, et al.Reliable Early Classification of Time Series Based on Discriminating the Classes Over Time[J]. Data Mining and Knowledge Discovery (S1384-5810), 2016, 31(1): 1-31.
[22] Mei J, Liu M, Wang Y F, et al.Learning a Mahalanobis Distance-based Dynamic Time Warping Measure for Multivariate Time Series Classification[J]. IEEE Transactions on Cybernetics (S2168-2267), 2016, 46(6): 1363-1374.
[23] Pérezgonzález A, Vergara M, Sanchobru J L, et al.Visualizing Data Using t-SNE[J]. Journal of Machine Learning Research (S1532-4435), 2008, 9: 2579-2605.