1 research outputs found
Autonomous Tracking of Intermittent RF Source Using a UAV Swarm
Localization of a radio frequency (RF) transmitter with intermittent
transmissions is considered via a group of unmanned aerial vehicles (UAVs)
equipped with omnidirectional received signal strength (RSS) 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 steepest descent posterior
Cramer-Rao lower bound (CRLB) path planning and a bio-inspired heuristic path
planning. Our results show that the steepest descent path planning outperforms
the bio-inspired path planning by an order of magnitude, and recursive Bayesian
estimator narrowly outperforms detection based EKF