UAV-Aided Progressive Interference Source Localization Based on Improved Trust Region Optimization
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866918001524080640 |
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| author | Gu, Guochen Lin, Zhipeng Zhu, Qiuming Chen, Junchang Wu, Qihui Duan, Hongtao Huang, Yang Zhong, Weizhi |
| author_facet | Gu, Guochen Lin, Zhipeng Zhu, Qiuming Chen, Junchang Wu, Qihui Duan, Hongtao Huang, Yang Zhong, Weizhi |
| contents | Trust region optimization-based received signal strength indicator (RSSI) interference source localization methods have been widely used in low-altitude research. However, these methods often converge to local optima in complex environments, degrading the positioning performance. This paper presents a novel unmanned aerial vehicle (UAV)-aided progressive interference source localization method based on improved trust region optimization. By combining the Levenberg-Marquardt (LM) algorithm with particle swarm optimization (PSO), our proposed method can effectively enhance the success rate of localization. We also propose a confidence quantification approach based on the UAV-to-ground channel model. This approach considers the surrounding environmental information of the sampling points and dynamically adjusts the weight of the sampling data during the data fusion. As a result, the overall positioning accuracy can be significantly improved. Experimental results demonstrate the proposed method can achieve high-precision interference source localization in noisy and interference-prone environments. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_19143 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | UAV-Aided Progressive Interference Source Localization Based on Improved Trust Region Optimization Gu, Guochen Lin, Zhipeng Zhu, Qiuming Chen, Junchang Wu, Qihui Duan, Hongtao Huang, Yang Zhong, Weizhi Signal Processing Trust region optimization-based received signal strength indicator (RSSI) interference source localization methods have been widely used in low-altitude research. However, these methods often converge to local optima in complex environments, degrading the positioning performance. This paper presents a novel unmanned aerial vehicle (UAV)-aided progressive interference source localization method based on improved trust region optimization. By combining the Levenberg-Marquardt (LM) algorithm with particle swarm optimization (PSO), our proposed method can effectively enhance the success rate of localization. We also propose a confidence quantification approach based on the UAV-to-ground channel model. This approach considers the surrounding environmental information of the sampling points and dynamically adjusts the weight of the sampling data during the data fusion. As a result, the overall positioning accuracy can be significantly improved. Experimental results demonstrate the proposed method can achieve high-precision interference source localization in noisy and interference-prone environments. |
| title | UAV-Aided Progressive Interference Source Localization Based on Improved Trust Region Optimization |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2504.19143 |