A multi-step Gaussian filtering approach to reduce the e ffect of non-Gaussian distribution in aerial localization of an RF source in NLOS condition


1 Advanced Robotics and Intelligent Systems Laboratory, School of Electrical and Computer Engineering, University of Tehran, Teran, Iran.

2 Intelligent Systems Research Institute, SKKU, South Korea.


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was implemented using particle lters due to the multi-modal/non-Gaussian nature of localization in NLOS condition, has been replaced by a multi-step Gaussian ltering approach which provides similar accuracy with better performance.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.