Full Text:   <396>

Summary:  <133>

CLC number: TN954; O224

On-line Access: 2019-04-09

Received: 2018-11-24

Revision Accepted: 2019-01-17

Crosschecked: 2019-03-14

Cited: 0

Clicked: 1734

Citations:  Bibtex RefMan EndNote GB/T7714


Yan-bo Zhu


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Frontiers of Information Technology & Electronic Engineering  2019 Vol.20 No.3 P.425-437


Optimized deployment of a radar network based on an improved firefly algorithm

Author(s):  Xue-jun Zhang, Wei Jia, Xiang-min Guan, Guo-qiang Xu, Jun Chen, Yan-bo Zhu

Affiliation(s):  National Key Laboratory of CNS/ATM, School of Electronic and Information Engineering, Beihang University, Beijing 100191, China; more

Corresponding email(s):   yanbo_zhu@163.com

Key Words:  Improved firefly algorithm, Radar surveillance network, Deployment optimization, Unmanned aerial vehicle (UAV) invasion defense

Xue-jun Zhang, Wei Jia, Xiang-min Guan, Guo-qiang Xu, Jun Chen, Yan-bo Zhu. Optimized deployment of a radar network based on an improved firefly algorithm[J]. Frontiers of Information Technology & Electronic Engineering, 2019, 20(3): 425-437.

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%DOI 10.1631/FITEE.1800749

T1 - Optimized deployment of a radar network based on an improved firefly algorithm
A1 - Xue-jun Zhang
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A1 - Xiang-min Guan
A1 - Guo-qiang Xu
A1 - Jun Chen
A1 - Yan-bo Zhu
J0 - Frontiers of Information Technology & Electronic Engineering
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PB - Zhejiang University Press & Springer
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DOI - 10.1631/FITEE.1800749

The threats and challenges of unmanned aerial vehicle (UAV) invasion defense due to rapid UAV development have attracted increased attention recently. One of the important UAV invasion defense methods is radar network detection. To form a tight and reliable radar surveillance network with limited resources, it is essential to investigate optimized radar network deployment. This optimization problem is difficult to solve due to its nonlinear features and strong coupling of multiple constraints. To address these issues, we propose an improved firefly algorithm that employs a neighborhood learning strategy with a feedback mechanism and chaotic local search by elite fireflies to obtain a trade-off between exploration and exploitation abilities. Moreover, a chaotic sequence is used to generate initial firefly positions to improve population diversity. Experiments have been conducted on 12 famous benchmark functions and in a classical radar deployment scenario. Results indicate that our approach achieves much better performance than the classical firefly algorithm (FA) and four recently proposed FA variants.




Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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