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CLC number: TN973.3
On-line Access: 2019-08-05
Received: 2018-06-25
Revision Accepted: 2019-05-09
Crosschecked: 2019-07-12
Cited: 0
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A novel algorithm to counter cross-eye jamming based on a multi-target model
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Zhi-yong Song, Xing-lin Shen, Qiang Fu. A novel algorithm to counter cross-eye jamming based on a multi-target model[J]. Frontiers of Information Technology & Electronic Engineering, 2019, 20(7): 988-1001.
@article{title="A novel algorithm to counter cross-eye jamming based on a multi-target model",
author="Zhi-yong Song, Xing-lin Shen, Qiang Fu",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="20",
number="7",
pages="988-1001",
year="2019",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.1800394"
}
%0 Journal Article
%T A novel algorithm to counter cross-eye jamming based on a multi-target model
%A Zhi-yong Song
%A Xing-lin Shen
%A Qiang Fu
%J Frontiers of Information Technology & Electronic Engineering
%V 20
%N 7
%P 988-1001
%@ 2095-9184
%D 2019
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.1800394
TY - JOUR
T1 - A novel algorithm to counter cross-eye jamming based on a multi-target model
A1 - Zhi-yong Song
A1 - Xing-lin Shen
A1 - Qiang Fu
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 20
IS - 7
SP - 988
EP - 1001
%@ 2095-9184
Y1 - 2019
PB - Zhejiang University Press & Springer
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DOI - 10.1631/FITEE.1800394
Abstract: cross-eye jamming is an electronic attack technique that induces an angular error in the monopulse radar by artificially creating a false target and deceiving the radar into detecting and tracking it. Presently, there is no effective anti-jamming method to counteract cross-eye jamming. In our study, through detailed analysis of the jamming mechanism, a multi-target model for a cross-eye jamming scenario is established within a random finite set framework. A novel anti-jamming method based on multi-target tracking using probability hypothesis density filters is subsequently developed by combining the characteristic differences between target and jamming with the releasing process of jamming. The characteristic differences between target and jamming and the releasing process of jamming are used to optimize particle partitioning. particle identity labels that represent the properties of target and jamming are introduced into the detection and tracking processes. The release of cross-eye jamming is detected by estimating the number of targets in the beam, and the distinction between true targets and false jamming is realized through correlation and transmission between labels and estimated states. Thus, accurate tracking of the true targets is achieved under severe jamming conditions. Simulation results showed that the proposed method achieves a minimum delay in detection of cross-eye jamming and an accurate estimation of the target state.
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