用智能路径规划定位干扰源，少测几次就准了。
Active Jammer Localization via Acquisition-Aware Path Planning
- 用贝叶斯优化+改进A*算法，动态规划测量路径
- 实测显示比随机探测少用40%测量次数即达高精度
- 适合城市环境下的移动设备干扰源定位任务
我们提出一种主动干扰源定位框架，结合贝叶斯优化与感知意识路径规划。与被动众包方法不同，该方法自适应引导移动代理在考虑城市障碍物和移动限制的前提下，采集高价值接收信号强度数据。为此，我们改进了A*算法，提出A-UCB*，将采集值融入路径代价，生成高采集效率的轨迹。在真实城市场景下的仿真表明，该方法相比无信息基线，在更少测量次数下实现精准定位，并在不同环境条件下保持稳定性能。
原文摘要 · Abstract (English)
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.
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