An Optimization Method for Multi-Functional Radar Network Deployment in Complex Regions
This paper addresses the deployment of a multi-functional radar network (MFRN) in complex regions that may exhibit non-connectivity, holes, or concave shapes, utilizing multi-objective particle swarm optimization (MOPSO). Unlike traditional approaches that rely on constraint-handling techniques, the...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-02-01
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| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/17/4/730 |
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| Summary: | This paper addresses the deployment of a multi-functional radar network (MFRN) in complex regions that may exhibit non-connectivity, holes, or concave shapes, utilizing multi-objective particle swarm optimization (MOPSO). Unlike traditional approaches that rely on constraint-handling techniques, the proposed methodology leverages the unique characteristics of polygonal deployment regions to enhance deployment efficiency. Specifically, for the aforementioned complex deployment regions, a region decomposition approach based on convex partitioning is proposed. This approach allows for the decomposition of complex regions into multiple non-overlapping convex subregions. Moreover, for convex deployment regions or subregions, we propose a coordinate transformation approach to eliminate the constraints introduced by the shape of the convex region. By combining the above approaches, we introduce a novel MOPSO based on decomposition and transformation, named MOPSO-DT. This algorithm aims to optimize MFRN deployment in these challenging environments. Experimental results demonstrate the superiority of the MOPSO-DT algorithm over two existing algorithms across a variety of deployment cases, highlighting its enhanced efficiency, effectiveness, and stability. These findings indicate that the proposed algorithm is well suited for optimizing MFRN deployment in complex, irregular regions, offering significant improvements in performance compared to conventional methods. |
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| ISSN: | 2072-4292 |