TOA/RSS-Based Source Localization Using Probabilistic Model in Mixed LOS/NLOS Environments

Abstract

When it comes to localization in urban or indoor environments, non-line of sight (NLOS) propagations and the associated multipath effects cannot be avoided, a phenomenon which significantly degrades the localization performance. To remedy the mentioned problem, this article proposes a novel localization method utilizing received signal strength (RSS) and time of arrival (TOA) measurements. To this end, the TOA/RSS hybrid maximum likelihood (ML) problem is cast into a probabilistic non-linear weighted least square (PNLWLS) problem, including probabilistic cost function and probabilistic constraints. Seven hypothesis tests are introduced to determine with what probability each measurement belongs to LOS (or mixed LOS/NLOS) or NLOS propagations. These probability values tightly bound the unknown NLOS error in the PNLWLS problem. The PNLWLS problem is then relaxed to a semi-definite programming (SDP) problem, which can be solved efficiently by interior point methods. Performance of the proposed method is evaluated for 5G technology using MATLAB 5G toolbox. Simulation results show that the proposed method significantly outperforms the existing hybrid methods and attains an estimation variance comparatively closer to the Cramer-Rao lower bound (CRLB).