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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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The hybrid localization using Angle Of Arrival (AOA) and Di erential Received Strength Signal Indicator (DRSSI) of an RF source with unknown power and Non-Line-Of-Sight (NLOS) condition has been proven to be advantageous compared to using each method separately. In this paper, the initial hybrid method, which was implemented using particle lters due to the multi-modal/non-Gaussian nature of localization in NLOS condition, has been replaced by a multi-step Gaussian ltering approach which provides similar accuracy with better performance. This has been done using DRSSI input in the rst step of the ltering to determine the linearization point, and then using AOA and DRSSI inputs together in the second step of the ltering to improve the localization accuracy. The proposed method has been implemented using Extended Kalman lter and Unscented Kalman lter. The simulation results show that the accuracy of the multi-step Gaussian ltering is comparable to the particle ltering approach with much lower computational load that is important for online localization of several RF sources. Furthermore, the e ects of uncertainty on the propagation parameters have been studied to show that the robustness of the multi-step Gaussian ltering to the uncertainties is comparable to the particle lter approach.