Autonomous trajectory control for limited number of aerial platforms in RF source localization

In this paper, an autonomous trajectory control is presented for minimum number of Unmanned Aerial Vehicles (UAVs) equipped with Received Signal Strength (RSS) sensors to localize a stationary Radio Frequency (RF) source. The RSS at each UAV is observed in specified time intervals. Due to the nonlinear observations the location of the source is estimated using the Extended Kalman Filter (EKF). The objective is to determine the waypoints for the UAVs that minimize the source location uncertainty. The waypoint updates are achieved from an iterative normalized gradient descent optimization algorithm. The UAV waypoints are determined by optimizing a cost function involving the mean-square error of filtered target position estimates produced by the extended Kalman filter. The effectiveness of the approach is depicted through simulation examples.