3D UAV Localization Optimization Under Jamming Attacks: A Mixture Gaussian Distribution Based Collaborative Reinforcement Learning

doi:10.1109/TMC.2025.3628889

3D UAV Localization Optimization Under Jamming Attacks: A Mixture Gaussian Distribution Based Collaborative Reinforcement Learning

2025-11-04

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DOI：10.1109/TMC.2025.3628889

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In this paper, the optimization of unmanned aerial vehicle (UAV) localization under jamming attacks is studied. In the considered network, a base station (BS) collaborates with an active UAV to localize a target UAV. During this positioning process, a jamming UAV transmits discontinuous signals to passive UAVs to interfere the distance information measurement. To localize the target UAV under jamming attacks, the BS jointly uses two localization methods: 1) generative adversarial network (GAN) based positioning method and 2) time difference of arrival (TDOA) based positioning method. Since GAN-based method cannot defend against a strong jamming signal while TDOA-based method may consume more energy and sacrifice localization accuracy, the BS must select an appropriate positioning method (GAN-based or TDOA-based methods) and four distance measurement information of passive UAVs to localize the target UAV. This problem is formulated as an optimization problem. The aim of this problem is to minimize the positioning error between the estimated and the ground truth positions of the target UAV while considering jamming attacks and the trajectory of passive UAVs. To solve this problem, we propose a mixture Gaussian distribution model based collaborative reinforcement learning (RL) method which enables the active UAV to optimize its transmit power and trajectory, and enables the BS to select the most appropriate subsets of distance measurement information and the optimal positioning method according to the UAVs movement and the unknown jamming attack pattern. Simulation results show the proposed method can reduce the positioning error of the target UAV by up to 36.5% compared to the method that does not consider the GAN-based positioning method.

Keywords:

UAV localization

jamming attacks

GAN-based positioning method

TDOA-based positioning method

3D UAV Localization Optimization Under Jamming Attacks: A Mixture Gaussian Distribution Based Collaborative Reinforcement Learning

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