Enhanced UWB Indoor Tracking through NLOS TOA Biases Estimation

Abstract

Reliable range-based tracking using Ultra-WideBand (UWB) radio signals is traditionally problematic in indoor environments. It is indeed widely admitted that Non Line-OfSight (NLOS) channel configurations introduce significant biases on measured temporal metrics such as Times Of Arrival (TOA), and hence, alter position estimates accordingly. In order to enhance the tracking performance, we propose a specific Extended Kalman Filter (EKF) formulation, which enables to estimate as state variables the NLOS biases affecting the measured distances between the Mobile Station (MS) and the fixed Base Stations (BSs). The described solution is mainly based on the modeling of the deterministic angular-dependent bias variation experienced with MS mobility. It is shown that this approach is robust to harmful situations where all the links between the MS and the BSs simultaneously suffer from NLOS. Simulation results are provided for performance assessment in a few canonical scenarios.

Adaptive Particle Filter for Nonparametric Estimation with Measurement Uncertainty in Wireless Sensor Networks

“…Jung et al identify the NLOS path for the TOA ranging and use PFs to estimate the target according to the biased error model [ 15 ]. Youssef et al provide the biased model of the RSS NLOS ranging error [ 16 ]. In addition, the map information can also help to indicate the NLOS path [ 17 ].…”