Metaheuristic Optimization for Robust RSSD-Based UAV Localization with Position Uncertainty
Unmanned aerial vehicles (UAVs) have garnered significant research interest across various fields due to their excellent maneuverability, scalability, and flexibility. However, potential collisions and other issues can disrupt communication and hinder functionality in real-world applications. Theref...
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MDPI AG
2025-02-01
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| Online Access: | https://www.mdpi.com/2504-446X/9/2/147 |
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| author | Yuanyuan Zhang Jiping Li T. Aaron Gulliver Huafeng Wu Guangqian Xie Xiaojun Mei Jiangfeng Xian Weijun Wang Linian Liang |
| author_facet | Yuanyuan Zhang Jiping Li T. Aaron Gulliver Huafeng Wu Guangqian Xie Xiaojun Mei Jiangfeng Xian Weijun Wang Linian Liang |
| author_sort | Yuanyuan Zhang |
| collection | DOAJ |
| description | Unmanned aerial vehicles (UAVs) have garnered significant research interest across various fields due to their excellent maneuverability, scalability, and flexibility. However, potential collisions and other issues can disrupt communication and hinder functionality in real-world applications. Therefore, accurate localization of UAVs is crucial. Nonetheless, environmental factors and inherent stability issues can lead to node positional errors in UAV networks, compounded by inaccuracies in transmit power estimation, complicating the effectiveness of signal strength-based localization methods in achieving high accuracy. To mitigate the adverse effects of these issues, a novel received signal strength difference (RSSD)-based localization scheme based on a robust enhanced salp swarm algorithm (RESSA) is presented. In this algorithm, an elitism strategy based on tent opposition-based learning (TOL) is proposed to promote the leader to move around the food source. Differential evolution (DE) is then used to enhance the exploration ability of each agent and improve global search. In addition, a dynamic movement mechanism for followers is designed, enabling the swarm to swiftly converge towards the food source, thereby accelerating the overall convergence process. The RSSD-based Cramér–Rao lower bound (CRLB) with position uncertainty is derived to evaluate the performance. Experimental results are presented, which show that the proposed RESSA provides better localization performance than related methods in the literature. |
| format | Article |
| id | doaj-art-ea4ba328d8a54c6fad7cf2f711baf6ed |
| institution | DOAJ |
| issn | 2504-446X |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Drones |
| spelling | doaj-art-ea4ba328d8a54c6fad7cf2f711baf6ed2025-08-20T02:44:46ZengMDPI AGDrones2504-446X2025-02-019214710.3390/drones9020147Metaheuristic Optimization for Robust RSSD-Based UAV Localization with Position UncertaintyYuanyuan Zhang0Jiping Li1T. Aaron Gulliver2Huafeng Wu3Guangqian Xie4Xiaojun Mei5Jiangfeng Xian6Weijun Wang7Linian Liang8School of Computer Information and Engineering, Changzhou Institute of Technology, Changzhou 213032, ChinaSchool of Computer Information and Engineering, Changzhou Institute of Technology, Changzhou 213032, ChinaDepartment of Electrical and Computer Engineering, University of Victoria, Victoria, BC V8W 2Y2, CanadaMerchant Marine College, Shanghai Maritime University, Shanghai 201306, ChinaSchool of Computer Information and Engineering, Changzhou Institute of Technology, Changzhou 213032, ChinaMerchant Marine College, Shanghai Maritime University, Shanghai 201306, ChinaInstitute of Logistics Science and Engineering, Shanghai Maritime University, Shanghai 201306, ChinaNavigation College, Jimei University, Xiamen 361021, ChinaMerchant Marine College, Shanghai Maritime University, Shanghai 201306, ChinaUnmanned aerial vehicles (UAVs) have garnered significant research interest across various fields due to their excellent maneuverability, scalability, and flexibility. However, potential collisions and other issues can disrupt communication and hinder functionality in real-world applications. Therefore, accurate localization of UAVs is crucial. Nonetheless, environmental factors and inherent stability issues can lead to node positional errors in UAV networks, compounded by inaccuracies in transmit power estimation, complicating the effectiveness of signal strength-based localization methods in achieving high accuracy. To mitigate the adverse effects of these issues, a novel received signal strength difference (RSSD)-based localization scheme based on a robust enhanced salp swarm algorithm (RESSA) is presented. In this algorithm, an elitism strategy based on tent opposition-based learning (TOL) is proposed to promote the leader to move around the food source. Differential evolution (DE) is then used to enhance the exploration ability of each agent and improve global search. In addition, a dynamic movement mechanism for followers is designed, enabling the swarm to swiftly converge towards the food source, thereby accelerating the overall convergence process. The RSSD-based Cramér–Rao lower bound (CRLB) with position uncertainty is derived to evaluate the performance. Experimental results are presented, which show that the proposed RESSA provides better localization performance than related methods in the literature.https://www.mdpi.com/2504-446X/9/2/147unmanned aerial vehicle (UAV)robust localizationreceived signal strength difference (RSSD)position uncertaintysalp swarm algorithm |
| spellingShingle | Yuanyuan Zhang Jiping Li T. Aaron Gulliver Huafeng Wu Guangqian Xie Xiaojun Mei Jiangfeng Xian Weijun Wang Linian Liang Metaheuristic Optimization for Robust RSSD-Based UAV Localization with Position Uncertainty Drones unmanned aerial vehicle (UAV) robust localization received signal strength difference (RSSD) position uncertainty salp swarm algorithm |
| title | Metaheuristic Optimization for Robust RSSD-Based UAV Localization with Position Uncertainty |
| title_full | Metaheuristic Optimization for Robust RSSD-Based UAV Localization with Position Uncertainty |
| title_fullStr | Metaheuristic Optimization for Robust RSSD-Based UAV Localization with Position Uncertainty |
| title_full_unstemmed | Metaheuristic Optimization for Robust RSSD-Based UAV Localization with Position Uncertainty |
| title_short | Metaheuristic Optimization for Robust RSSD-Based UAV Localization with Position Uncertainty |
| title_sort | metaheuristic optimization for robust rssd based uav localization with position uncertainty |
| topic | unmanned aerial vehicle (UAV) robust localization received signal strength difference (RSSD) position uncertainty salp swarm algorithm |
| url | https://www.mdpi.com/2504-446X/9/2/147 |
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