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

 ORCID:

Yan-bo Zhu

http://orcid.org/0000-0003-1691-2680

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

10.1631/FITEE.1800749


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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%A Xiang-min Guan
%A Guo-qiang Xu
%A Jun Chen
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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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EP - 437
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PB - Zhejiang University Press & Springer
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DOI - 10.1631/FITEE.1800749


Abstract: 
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.

基于改进萤火虫算法的雷达网络最优化部署

摘要:随着无人机行业快速发展,无人机入侵带来的威胁和挑战引起越来越多关注。雷达网络检测是无人机入侵防御的重要手段之一。为在有限雷达资源下形成紧密可靠的雷达监视网络,雷达网优化部署至关重要。然而,由于非线性且变量紧密耦合,雷达网部署优化问题难以解决。提出基于反馈机制的邻居学习策略和精英种群局部搜索策略的改进萤火虫算法,以平衡开发与探索能力,同时采用混沌序列初始化萤火虫位置,提高种群多样性。标准优化测试函数实验和典型雷达部署案例实验表明,该改进萤火虫算法比经典萤火虫算法和4个近期提出的改进算法有更好寻优性能,能有效解决雷达网部署优化问题。

关键词:改进萤火虫算法;雷达监视网络;部署优化;无人机入侵防御

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

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