In this paper, two trajectory control approaches are presented for any number of unmanned aerial vehicles (UAVs) in radio frequency (RF) source localization. The UAVs observe the received signal strength (RSS) in distinctive time intervals to localize a stationary RF source. The location of the source is estimated recursively applying the extended Kalman filter. The objective of the optimal trajectory control is to steer the UAVs to the locations which minimize the uncertainty about the target state. The Fisher information matrix (FIM) is inversely proportional to the estimation variance. Since the true target state is unknown, the FIM is approximated by the estimated target state. Two criteria based on the approximated FIM are applied to measure the information content of the observations to optimize the UAV waypoints: The D-optimality and the A-optimality. The objective of the present paper is to propose two trajectory control approaches for any number of UAVs in RSS-based localization to increase the target localization accuracy. The superiority of the trajectory optimization approach based on the D-optimality in terms of mean squared error is illustrated through simulation examples.

This paper is concerned with optimal trajectory control for two unmanned aerial vehicles (UAVs) in a multisource localization environment. The received signal strength (RSS) at the UAVs in specified time intervals permits passive differential RSS (DRSS)-based localization of multiple radio frequency (RF) sources with unknown transmit powers. A steering algorithm is proposed to update the UAV waypoints in order to minimize the summation of the uncertainty of the source locations. The UAV paths are optimized by maximizing the determinant of the Fisher Information Matrix (FIM). The FIM is approximated at successive waypoints using the estimated locations of the sources. In addition to maximizing the localization accuracy, the objectives of the proposed trajectory control are to minimize the number of UAVs, the mission time and the path length. As the DRSS is a non-linear measurement, an extended Kalman filter (EKF), which is a non-linear filtering technique, is considered in this paper. The efficiency of the approach is depicted through simulations.

The Radio Frequency (RF) source localization accuracy depends not only on the measurement performance of sensors, but also on the relative location of the sensors and the source. This paper investigates the impact of UAVs, equipped with RSSI sensors, formation and trajectory on the aerial RF source localization performance for Differential Received Signal Strength Indication (DRSSI) based approach in None Line Of Sight (NLOS) propagation condition. To eliminate the need for knowing the power of the signal source the DRSSI approach is applied. The collected measurements in each waypoint are used to estimate the location of the source iteratively by the use of Extended Kalman Filter (EKF). The Cramer-Rao Lower Bound (CRLB), which expresses a lower bound on the variance of any unbiased estimator, is used as the objective function for the proposed algorithm. Due to the complexity of Jacobin calculation to perform global CRLB optimization, the local values of CRLBs in the current waypoint and the next probable waypoints are used to determine the best path. Maximizing the determinant of the inverse of CRLB, i.e. the Fisher Information Matrix, in each measurement instance over the next probable waypoint, minimizes the estimation uncertainty area. The effectiveness of the proposed algorithm is illustrated by Monte-Carlo simulations and compared with the basic bio-inspired approach of going toward the estimated direction of the source.

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