Active Search and Tracking for search and rescue missions or collaborative mobile robotics relies on the actuation of a sensing platform to detect and localize a target. In this paper we focus on visually detecting a radio-emitting target with an aerial robot equipped with a radio receiver and a camera. Visual-based tracking provides high accuracy, but the directionality of the sensing domain often requires long search times before detecting the target. Conversely,radio signals have larger coverage, but lower tracking accuracy. Thus, we design a Recursive Bayesian Estimation scheme that uses camera observations to refine radio measurements. To regulate the camera pose, we design an optimal controller whose cost function is built upon a probabilistic map. Theoretical results support the proposed algorithm, while numerical analyses show higher robustness and efficiency with respect to visual and radio-only baselines.

中文翻译：

---

用于搜索和跟踪的概率无线电视觉主动感应

用于搜索和救援任务或协作移动机器人的主动搜索和跟踪依赖于感测平台的启动来检测和定位目标。在本文中，我们着重于通过配备有无线电接收器和照相机的空中机器人在视觉上检测无线电发射目标。基于视觉的跟踪提供了很高的准确性，但是感测域的方向性通常需要较长的搜索时间才能检测到目标。相反，无线电信号具有较大的覆盖范围，但跟踪精度较低。因此，我们设计了一种递归贝叶斯估计方案，该方案使用相机观测来完善无线电测量。为了调节摄像机的姿势，我们设计了一个最优控制器，其成本函数基于概率图。理论结果支持所提出的算法，