UAV-based multiple source localization and contour mapping of radiation fields

This paper proposes an efficient approach to the multiple source localization and contour mapping problem of radiation fields using Unmanned Aerial Vehicles (UAVs). A typical radiation field originating from a single hotspot can be generated by three spatial distributions of sources; scattered, clustered and biased. Of these, the clustered sources are relatively easy to localize, because the sources are located in a close proximity to the center of distribution. In other cases, it is not very straightforward, because, when multiple radiating sources generate a hotspot in a cumulative manner, sources do not coincide with the hotspot position. Regardless of our knowledge about the hotspot position, we attempt to solve the multiple radiation localization problem in two steps: the Region Of Interest (ROI) selection and the source localization. Existing algorithms eventually explore whole area, causing the problem of excessive use of UAV resources. We therefore propose a framework to reduce ROI in a radiation field that not only optimizes the resources but also increases the localization accuracy. For the source localization process, two different methods are employed interchangeably. Those methods are called the Hough Transform and the Variational Bayesian, adaptively selected with a switching technique and the overall performance is evaluated by balancing between the localization accuracy and the required exploration. In favor of the optimization, the prediction model defines the type of sources in a way that the adaptive switching methodology can converge to an optimal solution by selecting an appropriate method. Thus, the proposed framework enables the UAV to accurately localize the radiation sources in a fast manner. In order to verify the validity and the performance of the proposed strategies, we performed extensive numerical experiments with different numbers of sources and their positions. Our empirical results clearly show that the proposed approach outperforms existing individual approaches.