A Hybrid 3D Localization Algorithm Based on Meta-Heuristic Weighted Fusion
This paper presents a hybrid indoor localization framework combining time difference of arrival (TDoA) measurements with a swarm intelligence optimization technique. To address the nonlinear optimization challenges in three-dimensional (3D) indoor localization via TDoA measurements, we systematicall...
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MDPI AG
2025-07-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/13/15/2423 |
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| author | Dongfang Mao Guoping Jiang Yun Zhao |
| author_facet | Dongfang Mao Guoping Jiang Yun Zhao |
| author_sort | Dongfang Mao |
| collection | DOAJ |
| description | This paper presents a hybrid indoor localization framework combining time difference of arrival (TDoA) measurements with a swarm intelligence optimization technique. To address the nonlinear optimization challenges in three-dimensional (3D) indoor localization via TDoA measurements, we systematically evaluate the artificial bee colony (ABC) algorithm and chimpanzee optimization algorithm (ChOA). Through comprehensive Monte Carlo simulations in a cubic 3D environment with eight beacons, our comparative analysis reveals that the ChOA achieves superior localization accuracy while maintaining computational efficiency. Building upon the ChOA framework, we introduce a multi-beacon fusion strategy incorporating a local outlier factor-based linear weighting mechanism to enhance robustness against measurement noise and improve localization accuracy. This approach integrates spatial density estimation with geometrically consistent weighting of distributed beacons, effectively filtering measurement outliers through adaptive sensor fusion. The experimental results show that the proposed algorithm exhibits excellent convergence performance under the condition of a low population size. Its anti-interference capability against Gaussian white noise is significantly improved compared with the baseline algorithms, and its anti-interference performance against multipath noise is consistent with that of the baseline algorithms. However, in terms of dealing with UWB device failures, the performance of the algorithm is slightly inferior. Meanwhile, the algorithm has relatively good time-lag performance and target-tracking performance. The study provides theoretical insights and practical guidelines for deploying reliable localization systems in complex indoor environments. |
| format | Article |
| id | doaj-art-26dd2ee9c294432cb66ed66b0d76da02 |
| institution | Kabale University |
| issn | 2227-7390 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj-art-26dd2ee9c294432cb66ed66b0d76da022025-08-20T03:36:34ZengMDPI AGMathematics2227-73902025-07-011315242310.3390/math13152423A Hybrid 3D Localization Algorithm Based on Meta-Heuristic Weighted FusionDongfang Mao0Guoping Jiang1Yun Zhao2College of Automation, Nanjing University of Posts and Telecommunications, Nanjing 210023, ChinaCollege of Automation, Nanjing University of Posts and Telecommunications, Nanjing 210023, ChinaWuxi Realid Technology Co., Ltd., Wuxi 214135, ChinaThis paper presents a hybrid indoor localization framework combining time difference of arrival (TDoA) measurements with a swarm intelligence optimization technique. To address the nonlinear optimization challenges in three-dimensional (3D) indoor localization via TDoA measurements, we systematically evaluate the artificial bee colony (ABC) algorithm and chimpanzee optimization algorithm (ChOA). Through comprehensive Monte Carlo simulations in a cubic 3D environment with eight beacons, our comparative analysis reveals that the ChOA achieves superior localization accuracy while maintaining computational efficiency. Building upon the ChOA framework, we introduce a multi-beacon fusion strategy incorporating a local outlier factor-based linear weighting mechanism to enhance robustness against measurement noise and improve localization accuracy. This approach integrates spatial density estimation with geometrically consistent weighting of distributed beacons, effectively filtering measurement outliers through adaptive sensor fusion. The experimental results show that the proposed algorithm exhibits excellent convergence performance under the condition of a low population size. Its anti-interference capability against Gaussian white noise is significantly improved compared with the baseline algorithms, and its anti-interference performance against multipath noise is consistent with that of the baseline algorithms. However, in terms of dealing with UWB device failures, the performance of the algorithm is slightly inferior. Meanwhile, the algorithm has relatively good time-lag performance and target-tracking performance. The study provides theoretical insights and practical guidelines for deploying reliable localization systems in complex indoor environments.https://www.mdpi.com/2227-7390/13/15/2423indoor localizationtime difference of arrivalswarm intelligence optimizationmulti-beacon fusionlocal outlier factor |
| spellingShingle | Dongfang Mao Guoping Jiang Yun Zhao A Hybrid 3D Localization Algorithm Based on Meta-Heuristic Weighted Fusion Mathematics indoor localization time difference of arrival swarm intelligence optimization multi-beacon fusion local outlier factor |
| title | A Hybrid 3D Localization Algorithm Based on Meta-Heuristic Weighted Fusion |
| title_full | A Hybrid 3D Localization Algorithm Based on Meta-Heuristic Weighted Fusion |
| title_fullStr | A Hybrid 3D Localization Algorithm Based on Meta-Heuristic Weighted Fusion |
| title_full_unstemmed | A Hybrid 3D Localization Algorithm Based on Meta-Heuristic Weighted Fusion |
| title_short | A Hybrid 3D Localization Algorithm Based on Meta-Heuristic Weighted Fusion |
| title_sort | hybrid 3d localization algorithm based on meta heuristic weighted fusion |
| topic | indoor localization time difference of arrival swarm intelligence optimization multi-beacon fusion local outlier factor |
| url | https://www.mdpi.com/2227-7390/13/15/2423 |
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