Active Jammer Localization via Acquisition-Aware Path Planning
This work addresses jammer localization for mobile agents in urban settings, representing an incremental improvement over passive methods.
The paper tackles the problem of localizing jammers in urban environments by proposing an active framework that guides a mobile agent to collect high-utility signal measurements, achieving accurate localization with fewer measurements compared to uninformed baselines.
We propose an active jammer localization framework that combines Bayesian optimization with acquisition-aware path planning. Unlike passive crowdsourced methods, our approach adaptively guides a mobile agent to collect high-utility Received Signal Strength measurements while accounting for urban obstacles and mobility constraints. For this, we modified the A* algorithm, A-UCB*, by incorporating acquisition values into trajectory costs, leading to high-acquisition planned paths. Simulations on realistic urban scenarios show that the proposed method achieves accurate localization with fewer measurements compared to uninformed baselines, demonstrating consistent performance under different environments.