Emitter Signal Modulation Recognition Based on Joint Projection Dictionary Learning

doi:10.3969/j.issn.1000-1093.2020.07.026

Abstract

Abstract: An emitter signal recognition method based on joint projection dictionary learning (JPDL) is proposed for the limited atomic representation ability and the insufficient adaptability of complex environment for dictionary learning. The initial features of emitter signal are extracted by time-frequency transform, and the feature preprocessing is realized by dimensionality reduction and noise reduction. Then the atomic structure of the dictionary is optimized by using the methods of kernel space projection and dimensionality reduction projection, and a joint projection dictionary is obtained through data set training. The validity verification is completed by the classification test. The simulated results show that the extracted dictionary atoms have strong representation ability and can adapt to the complex environment with variable parameters. Compared with the conventional supervised dictionary learning method, the proposed method can better distinguish the multi-type and high-similarity signals. The overall recognition rate of 10 types of emitter signals, such as single carrier frequency modulation (SCFM) signal, linear frequency modulation (LFM) signal, nonlinear frequency modulation (NLFM) signal, binary phase shift keying (BPSK) signal, quadrature phase shift keying (QPSK) signal, Frank signal, binary frequency shift keying (BFSK） signal, quadrature frequency shift keying (QFSK) signal, LFM-BPSK modulation signal and BFSK-BPSK modulation signal, at -6 dB is 94.4%. Key

Key words: emittersignalrecognition, jointprojectiondictionarylearning, time-frequencyfeature, dimensionalityreduction, sparsecoding

CLC Number:

TN971⁺.1

LI Dongjin， YANG Ruijuan， DONG Ruijie. Emitter Signal Modulation Recognition Based on Joint Projection Dictionary Learning[J]. Acta Armamentarii, 2020, 41(7): 1464-1472.

References

［1］马定坤, 匡银, 杨新权. 侦干探通一体化现状与关键技术研究［J］. 中国电子科学研究院学报, 2016, 11(5): 457-462.
MA D K, KUANG Y, YANG X Q. Key issues and status research of integrated reconnaissance, interference, detection and communications［J］. Journal of China Academy of Electronics and Information Technology, 2016, 11(5): 457-462. (in Chinese)
［2］ GUO Q, NAN P L, WAN J. Radar signal recognition based on ambiguity function features and cloud model similarity［C］∥Proceedings of the 8th International Conference on Ultrawideband and Ultrashort Impulse Signals. Odessa, Ukraine: IEEE, 2016: 128-134.
［3］ ZHANG G X, JIN W D, HU L Z. Radar emitter signal recognition based on complexity features［J］. Journal of Southwest Jiaotong University, 2004, 12(2): 116-122.
［4］ THAYAPARAN T, STANKOVIC L, AMIN M, et al. Time-frequency approach to radar detection, imaging, and classification［J］. IET Signal Processing, 2010, 4(4): 325-328.
［5］ YANG L B, ZHANG S S, XIAO B. Radar emitter signal recognition based on time-frequency analysis［C］∥ Proceedings of IET International Radar Conference 2013. Xi'an, China: IET, 2013.
［6］ BAO C L, JI H, QUAN Y H, et al. Dictionary learning for sparse coding: algorithms and convergence analysis［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2016, 38(7): 1356-1369.
［7］ SONG J Q, XIE X M, SHI G M, et al. Multi-layer discriminative dictionary learning with locality constraint for image classification［J］. Pattern Recognition, 2019, 91: 135-146.
［8］ ZHAO Z, FENG G C, ZHANG L F, et al. Novel orthogonal based collaborative dictionary learning for efficient face recognition［J］. Knowledge-Based Systems, 2019, 163: 533-545.
［9］ ZHANG Z, XU Y, YANG J, et al. A survey of sparse representation: algorithms and applications［J］. IEEE Access, 2015, 3:490- 530.

［10］ ZHANG Q, LI B X. Discriminative K-SVD for dictionary learning in face recognition［C］∥Proceedings of the 23rd IEEE Conference on Computer Vision and Pattern Recognition. San Francisco, CA, US: IEEE, 2010: 2691-2698.
［11］ YANG M, ZHANG L, FENG X C, et al. Fisher discrimination dictionary learning for sparse representation［C］∥ Proceedings of IEEE International Conference on Computer Vision. Barcelona, Spain: IEEE, 2011.
［12］ JIANG Z L, LIN Z, DAVIS L S. Label consistent K-SVD: learning a discriminative dictionary for recognition［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(11): 2651-2664.
［13］ VAN N H, PATEL V M, NASRABADI N M, et al. Design of non-linear kernel dictionaries for object recognition［J］. IEEE Transactions on Image Processing, 2013, 22(12): 5123-5135.
［14］朱明, 金炜东, 胡来招. 一种基于Spectrum原子的雷达辐射源信号识别方法［J］. 电子与信息学报, 2009, 31(1):188-191.
ZHU M, JIN W D, HU L Z. A novel method for radar emitter signals recognition based on spectrum atoms［J］. Journal of Electronics & Information Technology, 2009, 31(1):188-191. (in Chinese)
［15］吴笑天, 王星, 王志鹏, 等. 基于Fisher判别字典学习的辐射源调制特征识别［J］. 兵工学报, 2018, 39(3):553-559.
WU X T, WANG X, WANG Z P, et al. Emitter signal modulation feature recognition based on fisher discrimination dictionary learning［J］. Acta Armamentarii, 2018, 39(3):553-559. (in Chinese)
［16］ AUGER F, FLANDRIN P. Improving the readability of time-frequency and time-scale representations by the reassignment method［J］. IEEE Transactions on Signal Processing, 1995, 43(5): 1068-1089.
［17］ ZHANG L, ZHOU W D, CHANG P C, et al. Kernel sparse representation-based classifier［J］. IEEE Transactions on Signal Processing, 2012, 60(4):1684-1695.
［18］ ZHANG H C, ZHANG Y N, HUANG T S. Simultaneous discriminative projection and dictionary learning for sparse representation based classification［J］. Pattern Recognition, 2013, 46(1): 346-354.
［19］ ZHANG G Q, SUN H J, JI Z X, et al. Kernel dictionary learning based discriminant analysis［J］. Journal of Visual Communication and Image Representation, 2016, 40(8):470-484.
［20］ YANG B Q, GU C C, WU K J, et al. Simultaneous dimensionality reduction and dictionary learning for sparse representation based classification［J］. Multimedia Tools and Applications, 2017, 76:8969-8990.
［21］ WANG J, KWON S, SHIM B. Generalized orthogonal matching pursuit［J］. IEEE Transactions on Sinal Processing, 2012, 60(12): 6202-6216.
［22］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):77-82.
ZHOU Z W, HUANG G M, GAO J, et al. Radar emitter identification algorithm based on deep learning［J］. Journal of Xidian University, 2017, 44(3):77-82. (in Chinese)

［23］ YANG A Y, SASTRY S S, GANESH A, et al. Fast l₁-minimization algorithms and an application in robust face recognition: a review［C］∥Proceedings of 2010 IEEE International Conference on Image Processing. Hong Kong, China: IEEE, 2010: 1849-1852.



[1]	WU Zeliang， YE Jianchuan， WANG Jiang， JIN Ren. Parameter Dimensionality Reduction and Optimal Design of Aircraft Airfoil Based on Deep Autoencoder Neural Network [J]. Acta Armamentarii, 2022, 43(6): 1326-1336.
[2]	PU Yunwei， LIU Taotao， GUO Jiang， WU Haixiao. Radar Emitter Signal Recognition Based on Convolutional Neural Network and Coordinate Transformation of AmbiguityFunction Main Ridge [J]. Acta Armamentarii, 2021, 42(8): 1680-1689.
[3]	LI Dongjin， YANG Ruijuan， LI Xiaobai， ZHU Shengkun， FEI Taiyong. Emitter Signal Modulation Recognition Based on Stacked Dimension Reduction and Dictionary Learning [J]. Acta Armamentarii, 2020, 41(10): 2023-2032.
[4]	MENG Guang-wei， WEI Tong-hui， ZHOU Li-ming， LI Feng. Hybrid Reliability Analysis of Structures Based on Taylor Expansion Method [J]. Acta Armamentarii, 2018, 39(7): 1404-1410.