Abstract
The localization of a radio-frequency transmitter with intermittent transmissions is considered via a group of unmanned aerial vehicles (UAVs) equipped with omnidirectional received signal strength sensors. This group embarks on an autonomous patrol to localize and track the target with a specified accuracy, as quickly as possible. The challenge can be decomposed into two stages: 1) estimation of the target position given previous measurements (localization) and 2) planning the future trajectory of the tracking UAVs to get lower expected localization error given current estimation (path planning). For each stage, we compare two algorithms in terms of performance and computational load. For the localization stage, we compare a detection-based extended Kalman filter (EKF) and a recursive Bayesian estimator. For the path planning stage, we compare a steepest descent posterior Cramer-Rao lower bound path planning and a bioinspired heuristic path planning. Our results show that the steepest descent path planning outperforms the bioinspired path planning by an order of magnitude, and recursive Bayesian estimator narrowly outperforms detection-based EKF.