Multi-Static Radar System Deployment Within a Non-Connected Region Utilising Particle Swarm Optimization
This paper is mainly devoted to studying the deployment problem of a multi-static radar system (MSRS) within a non-connected deployment region using multi-objective particle swarm optimization (MOPSO). By modeling and reformulating the problem, it can be represented as a multi-objective mixed intege...
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MDPI AG
2024-10-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/16/21/4004 |
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| author | Yi Han Xueting Li Tianxian Zhang Xiaobo Yang |
| author_facet | Yi Han Xueting Li Tianxian Zhang Xiaobo Yang |
| author_sort | Yi Han |
| collection | DOAJ |
| description | This paper is mainly devoted to studying the deployment problem of a multi-static radar system (MSRS) within a non-connected deployment region using multi-objective particle swarm optimization (MOPSO). By modeling and reformulating the problem, it can be represented as a multi-objective mixed integer programming (MOMIP), which eliminates the need for additional constraints. To enhance the algorithm performance, integer variables and continuous ones are treated separately employing multiple velocity formulas. The velocity formulas for integer variables are modified using the sigmoid function and genetic operation, leading to the proposal of two MSRS deployment algorithms, namely MOPSO-Sigmoid and MOPSO-Gene. To evaluate the performance of the proposed algorithms, they are compared with two existing MOPSO-based algorithms. The first algorithm is the MSRS deployment algorithm for the non-connected deployment region that addresses the additional constraint problem model. The second algorithm is based on an existing conventional MOPSO algorithm and addresses the equivalent MOMIP problem model. A numerical study demonstrates that MOPSO-Sigmoid and MOPSO-Gene exhibit promising efficiency and effectiveness. |
| format | Article |
| id | doaj-art-3ceddc93cf9d4eba81bc3c236daf66ca |
| institution | DOAJ |
| issn | 2072-4292 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-3ceddc93cf9d4eba81bc3c236daf66ca2025-08-20T02:49:49ZengMDPI AGRemote Sensing2072-42922024-10-011621400410.3390/rs16214004Multi-Static Radar System Deployment Within a Non-Connected Region Utilising Particle Swarm OptimizationYi Han0Xueting Li1Tianxian Zhang2Xiaobo Yang3School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, ChinaSchool of Aeronautics and Astronautics, Sichuan University, Chengdu 610065, ChinaSchool of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, ChinaSchool of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, ChinaThis paper is mainly devoted to studying the deployment problem of a multi-static radar system (MSRS) within a non-connected deployment region using multi-objective particle swarm optimization (MOPSO). By modeling and reformulating the problem, it can be represented as a multi-objective mixed integer programming (MOMIP), which eliminates the need for additional constraints. To enhance the algorithm performance, integer variables and continuous ones are treated separately employing multiple velocity formulas. The velocity formulas for integer variables are modified using the sigmoid function and genetic operation, leading to the proposal of two MSRS deployment algorithms, namely MOPSO-Sigmoid and MOPSO-Gene. To evaluate the performance of the proposed algorithms, they are compared with two existing MOPSO-based algorithms. The first algorithm is the MSRS deployment algorithm for the non-connected deployment region that addresses the additional constraint problem model. The second algorithm is based on an existing conventional MOPSO algorithm and addresses the equivalent MOMIP problem model. A numerical study demonstrates that MOPSO-Sigmoid and MOPSO-Gene exhibit promising efficiency and effectiveness.https://www.mdpi.com/2072-4292/16/21/4004multi-static radar system (MSRS) deploymentnon-connected deployment regionmulti-objective particle swarm optimization (MOPSO)multi-objective segmented decision variable problem (MOSDVP)multi-objective mixed integer programming (MOMIP)multiple velocity formula |
| spellingShingle | Yi Han Xueting Li Tianxian Zhang Xiaobo Yang Multi-Static Radar System Deployment Within a Non-Connected Region Utilising Particle Swarm Optimization Remote Sensing multi-static radar system (MSRS) deployment non-connected deployment region multi-objective particle swarm optimization (MOPSO) multi-objective segmented decision variable problem (MOSDVP) multi-objective mixed integer programming (MOMIP) multiple velocity formula |
| title | Multi-Static Radar System Deployment Within a Non-Connected Region Utilising Particle Swarm Optimization |
| title_full | Multi-Static Radar System Deployment Within a Non-Connected Region Utilising Particle Swarm Optimization |
| title_fullStr | Multi-Static Radar System Deployment Within a Non-Connected Region Utilising Particle Swarm Optimization |
| title_full_unstemmed | Multi-Static Radar System Deployment Within a Non-Connected Region Utilising Particle Swarm Optimization |
| title_short | Multi-Static Radar System Deployment Within a Non-Connected Region Utilising Particle Swarm Optimization |
| title_sort | multi static radar system deployment within a non connected region utilising particle swarm optimization |
| topic | multi-static radar system (MSRS) deployment non-connected deployment region multi-objective particle swarm optimization (MOPSO) multi-objective segmented decision variable problem (MOSDVP) multi-objective mixed integer programming (MOMIP) multiple velocity formula |
| url | https://www.mdpi.com/2072-4292/16/21/4004 |
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