UAV-Assisted Wireless Localization for Search and Rescue

Abstract

In the aftermath of disasters, localization of trapped victims is imperative to ensure their safety and rescue. This article presents a novel localization and path planning approach that uses unmanned aerial vehicles (UAVs). The UAVs can extract one-hop neighbor information from the devices that may have run out of power by using directed wireless power transfer (WPT). The one-hop neighbor information corresponds to range measurements, which may or may not contain noise. For the noiseless case, we present a customized online graph traversal approach that minimizes the search energy of the UAV and the number of unlocalized nodes. The lower limits on the various performance aspects of this joint approach are presented. For a noiseless case, the results of UAV travel distance and cells searched show a decreasing trend with an increase in the number of maximum neighbors. These curves approximately approach their corresponding lower limits when the number of maximum neighbors is increased beyond 9. For the case of noisy range measurements, using the same objective function and graph traversal algorithm, the probabilistic region for search is determined that gives the least probability of flip errors. To this end, we further optimize the UAV flight path and its search energy in the probabilistic region through clustering. The proposed method is able to achieve linear scaling of the area searched with respect to the noise level. For a given noise level and increasing number of nodes, the UAV search energy with clustering can reduce the energy cost to 70%.