Active Jammer Localization via Acquisition-Aware Path Planning

2025-10-16

AI Summary

To address the low efficiency of wireless jammer localization in urban environments—characterized by dense obstacles and motion constraints—this paper proposes an active-sensing Bayesian optimization framework. The core innovation lies in explicitly incorporating the acquisition value into the path cost function, yielding the A-UCB* algorithm: a trajectory planning method that integrates Bayesian optimization with an enhanced A* search to adaptively generate measurement paths with high information gain, while respecting kinematic constraints and obstacle avoidance. Simulation results on realistic urban scenarios demonstrate that the proposed approach significantly reduces the required number of measurements (by ~40% compared to unguided baselines), improves localization accuracy and robustness, and exhibits strong generalization across varying jammer strengths and environmental layouts.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Constraint Satisfaction and Optimization: Constraint Learning and Acquisition
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Security and Privacy: Large-scale security measurements

Abstract

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.

Problem

• Localizing active jammers using Bayesian optimization and path planning
• Adaptively guiding mobile agents to collect high-utility signal measurements
• Achieving accurate localization with fewer measurements in urban scenarios

Innovation

• Combines Bayesian optimization with path planning
• Modifies A* algorithm with acquisition values
• Guides mobile agent for high-utility measurements