Radio source localization can benefit many fields, including wireless communications, radar, radio astronomy, wireless sensor networks, positioning systems, and surveillance systems. However, accurately estimating the position of a radio transmitter using a remote sensor is not an easy task, as many factors contribute to the highly dynamic behavior of radio signals. In this study, we investigate techniques to use a mobile robot to explore an outdoor area and localize the radio source using sparse Received Signal Strength Indicator (RSSI) measurements. We propose a novel radio source localization method with fast turnaround times and reduced complexity compared to the state-of-the-art. Our technique uses RSSI measurements collected while the robot completed a sparse trajectory using a coverage path planning map. The mean RSSI within each grid cell was used to find the most likely cell containing the source. Three techniques were analyzed with the data from eight field tests using a mobile robot. The proposed method can localize a gas source in a basketball field with a 1.2 m accuracy and within three minutes of convergence time, whereas the state-of-the-art active sensing technique took more than 30 minutes to reach a source estimation accuracy below 1 m.

翻译：无线电源定位可为无线通信、雷达、射电天文学、无线传感器网络、定位系统及监控系统等多个领域带来效益。然而，利用远程传感器准确估计无线电发射机的位置并非易事，因为诸多因素会导致无线电信号呈现高度动态行为。本研究探究了利用移动机器人探索室外区域，并通过稀疏接收信号强度指示（RSSI）测量值定位无线电源的技术。我们提出了一种新型无线电源定位方法，与现有技术相比，该方法具有更快的周转时间和更低的复杂度。该技术利用机器人按照覆盖路径规划地图完成稀疏轨迹时采集的RSSI测量值，通过计算每个网格单元内的平均RSSI来定位最可能包含信号源的单元。基于移动机器人八次现场试验的数据，我们对三种技术进行了分析。所提出的方法能够在1.2米精度范围内定位篮球场上的气源，且收敛时间在3分钟以内；而现有的主动感知技术则需要30分钟以上才能达到低于1米的源估计精度。