Deployment of multistatic radar system using multi‐objective particle swarm optimisation. Issue 5 (2nd March 2018)

• Record Type: Journal Article
• Authors: Yang, Yichuan; Zhang, Tianxian; Yi, Wei; Kong, Lingjiang; Li, Xiaolong; Wang, Bing; Yang, Xiaobo
• Is Part Of: IET radar, sonar & navigation. Volume 12:Issue 5(2018)
• Page Start: 485
• Page End: 493
• Publication Date: 2018-03-02
• Subjects: particle swarm optimisation -- radar antennas -- radar detection -- search radar; multistatic radar system deployment -- antenna placement -- transmitted power allocation -- MSRS performance -- detection method -- surveillance region coverage ratio improvement -- signal energy distribution -- MOPSO algorithm -- multiobjective particle swarm optimisation algorithm with non‐dominated relative crowding distance -- MOPSO‐NRCD -- crowding distance normalisation -- nondominative relationship comparison -- multiswarm structure; Signal processing -- Periodicals; Radar -- Periodicals; Sonar -- Periodicals; Electronics in navigation -- Periodicals; Navigation -- Periodicals; 621.3848
• DOI: 10.1049/iet-rsn.2017.0351
• Languages: English
• ISSNs: 1751-8784

Abstract

Abstract : The authors consider an optimisation problem of multistatic radar system (MSRS) deployment, which includes both the antenna placement and the transmitted power allocation. To improve the surveillance performance of MSRS that equipped with different detection methods, they mainly aim at two goals: (i) to improve the coverage ratio of a surveillance region; (ii) to get an even distribution of signal energy in the surveillance region. Through introducing two objective functions for the above two mentioned goals, respectively, they formulate a multi‐objective optimisation problem. To overcome drawbacks caused by the significant difference between the values of these two objective functions, they propose a multi‐objective particle swarm optimisation (MOPSO) algorithm with non‐dominated relative crowding distance (MOPSO‐NRCD). Specifically, through the non‐dominative relationship comparison and normalisation of crowding distance, a novel method is proposed to select the global best solution. Moreover, a multi‐swarm structure is applied to labour division of all particles. Comparing with the MOPSO with the traditional crowding distance, simulation results show that the MOPSO‐NRCD can provide better candidate schemes, which are capable to satisfy more stringent performance requirements.