Countermeasures for range false target jamming based on quasi Karhunen-Loève translate basis dictionary learning method

YU Lexin; ZHANG Hui; GONG Linshu; JIANG Tao

doi:10.19693/j.issn.1673-3185.02296

Volume 17 Issue 2

Apr. 2022

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Article Navigation > Chinese Journal of Ship Research > 2022 > 17(2): 228-232

YU L X, ZHANG H, GONG , et al. Countermeasures for range false target jamming based on quasi Karhunen-Loève translate basis dictionary learning method[J]. Chinese Journal of Ship Research, 2022, 17(2): 228–232 doi: 10.19693/j.issn.1673-3185.02296

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Countermeasures for range false target jamming based on quasi Karhunen-Loève translate basis dictionary learning method

doi: 10.19693/j.issn.1673-3185.02296

YU Lexin^1
,,
ZHANG Hui¹,
GONG Linshu²,
JIANG Tao^{1
,
,}

1.
College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China
2.
Shanghai Electro-Mechanical Engineering Institute, Shanghai 201109, China

Received Date: 2021-02-22
Rev Recd Date: 2021-06-07

Available Online: 2022-04-01

Publish Date: 2022-04-20

Abstract

This is an Open Access article distributed under the terms of the Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Objectives Waveform diversity technology is an effective anti-jamming measure against range false target jamming. However, in an environment of strong energy jamming, the high side-lobe caused by the jamming signal mismatch will still affect the detection performance of the radar. To this end, a dictionary learning method is proposed in order to better suppress and eliminate high-power jamming. Methods First, an initialization dictionary corresponding to the target and jamming signals is established. Second, the initialization dictionary and selected atoms are used to generate an autocorrelation matrix template, and the matching coefficient is obtained using the non-homogeneous linear mean square estimation. Next, an approximate quasi-Karhunen-Loève transform（Q-KLT） basis corresponding to the target and jamming signals is constructed by template and matching coefficients respectively. Finally, a convex optimization algorithm is used to separate and recover the target and jamming signals. Results The simulation results show that the proposed method can effectively counter the jamming of one or multiple range false targets at a 30 dB jamming-to-signal ratio. Conclusions Compared with the traditional waveform diversity technology, the proposed method still maintains good anti-jamming performance in high jamming-to-signal ratio environments, and can be used by shipboard radar to counter range false target jamming.
- anti-jamming of range false target,
- waveform diversity,
- dictionary learning,
- quasi Karhunen-Loève transform

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