UAV-Aided Progressive Interference Source Localization Based on Improved Trust Region Optimization
Abstract
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.
Keywords
Cite
@article{arxiv.2504.19143,
title = {UAV-Aided Progressive Interference Source Localization Based on Improved Trust Region Optimization},
author = {Guochen Gu and Zhipeng Lin and Qiuming Zhu and Junchang Chen and Qihui Wu and Hongtao Duan and Yang Huang and Weizhi Zhong},
journal= {arXiv preprint arXiv:2504.19143},
year = {2025}
}