Work Mode Identification of Phased Array Radar with Denoising Auto-encoder

doi:10.16182/j.issn1004731x.joss.201709012

Journal of System Simulation ›› 2017, Vol. 29 ›› Issue (9): 1960-1967.doi: 10.16182/j.issn1004731x.joss.201709012

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Work Mode Identification of Phased Array Radar with Denoising Auto-encoder

Liu Haodong, Jin Weidong, Chen Chunli, Cai Jian

School of Electrical Engineering, Southwest Jiaotong University, Chengdu 610031, China

Received:2017-06-15 Online:2017-09-08 Published:2020-06-02
About author:Liu Haodong (1993-), Suzhou, China, master student, research direction is pattern identification. Jin Weidong (1959-), Anhui Province, China, professor and Ph.D. supervisor, research direction is pattern identification and intelligent information processing.

Abstract

Abstract: A new method to recognize phased array radar in different work modes was proposed based on multi-level modeling combined with Marginalized Stacked Denoising Auto-encoder. In order to analyze the change law of pulses intercepted by surveillance radar, multi-level modeling was proposed to model the pulses at pulse level, pulse group level and work mode level. Marginalized stacked denoising auto-encoder was trained to extract amplitude characteristics at the work mode level. SVM (Support Vector Machine) was added to the top of deep network to realize work mode identification of phased array radar. Qualitative experiments show that the new method is able to extract essential characteristics of the input with its accuracy over 95%, which provides a new idea for mode identification of phased array radar.

Key words: phased array radar, work mode, multi-level modeling, marginalized auto-encoder

CLC Number:

TP183

Liu Haodong, Jin Weidong, Chen Chunli, Cai Jian. Work Mode Identification of Phased Array Radar with Denoising Auto-encoder[J]. Journal of System Simulation, 2017, 29(9): 1960-1967.

References

[1] Brookner E.Phased Arrays around the World Progress and Future Trends[C]// Proceedings of the 6th IEEE Phased Array Systems and Technology Symposium, Los Angeles, USA. USA: IEEE, 2003: 1-8.
[2] Moore S.UK airborne AESA Radar Research[J]. Aerospace & Electronic Systems Magazine IEEE (S1097-5764), 2010, 25(2): 29-35.
[3] Jia Chaowen, Zhou Shuilou.Work Mode Identification of Airborne Radar[J]. Electronic Information Warfare Technology (S1674-2230), 2011, 26(1): 14-16.
[4] Visnevski N, Krishnamurthy V, Wang A, et al.Syntactic Modeling and Signal Processing of Multifunction Radars: A Stochastic Context-Free Grammar Approach[J]. Proceedings of the IEEE (S0018-9219), 2007, 95(5): 1000-1025.
[5] Liu H D, Yu H, Sun Z, et al.Multi-function Radar Emitter Identification Based on Stochastic Syntax-directed Translation Schema[J]. Chinese Journal of Aeronautics (S1000-9361), 2014, 27(6): 1505-1512.
[6] Hinton G E, Osindero S, Whye Teh Y.A Fast Learning Algorithm for Deep Belief Nets[J]. Neural Computation (S0899-7667), 2006, 18(7): 1527-1554.
[7] Le Q V, Ngiam J, Coates A, et al.On optimization methods for deep learning[C]// International Conference on Machine Learning, ICML 2011, Bellevue, Washington, USA, June 28 - July. German: DBLP, 2011:265-272.
[8] Vincent P, Larochelle H, Lajoie I, et al.Stacked Denoising Auto-encoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion[J]. Journal of Machine Learning Research (S1532-4435), 2010, 11(12): 3371-3408.
[9] Vincent P, Larochelle H, Bengio Y, et al.Extracting and Composing Robust Features with Denoising Auto-encoders[C]// International Conference on Machine Learning. USA: ACM, 2008:1096-1103.
[10] Chandra B, Sharma R K.Adaptive Noise Schedule for Denoising Auto-encoder [C]// International Conference on Neural Information Processing. Switzerland: Springer International Publishing, 2014: 535-542.

Work Mode Identification of Phased Array Radar with Denoising Auto-encoder

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