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 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.

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Publication Details

Journal
Remote Sensing
Published
2024-10-28
DOI
https://doi.org/10.3390/rs16214004
Citations
5
Primary Topic
Advanced Multi-Objective Optimization Algorithms
Type
article
Field-Weighted Citation Impact
1.81

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article

Multi-Static Radar System Deployment Within a Non-Connected Region Utilising Particle Swarm Optimization

Yi Han, Tianxian Zhang, Xueting Li, Xiaobo Yang
5 citations
Remote Sensing
Advanced Multi-Objective Optimization Algorithms
1.81
article

Multi-Static Radar System Deployment Within a Non-Connected Region Utilising Particle Swarm Optimization

Yi Han, Tianxian Zhang, Xueting Li, Xiaobo Yang
article en
5 citations

Abstract

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.

Remote SensingVol. 16(21)
University of Electronic Science and Technology of China (CN), Sichuan University (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 13%
Advanced Multi-Objective Optimization Algorithms
1.81
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