Experimental study of indoor tracking using UWB measurements and particle filtering

Abstract

Abstract-Target tracking with ultra-wideband (UWB) signals in indoor environments is a challenging problem due to the presence of multipath and non-line-of-sight conditions (NLOS). A solution to this problem is to use particle filtering (PF), which is able to handle both nonlinear models and non-Gaussian uncertainties that typically appear in the presence of NLOS. In this paper, we compare four different PF variants, that differ in terms of how NLOS measurements are handled. According to our experimental results, based on the measurements from a basement tunnel, multiple features from the UWB impulse response should be used, and the ranging likelihood function should make use of both LOS and NLOS measurements. Standard timeof-arrival (TOA) based methods, even with NLOS rejection, are not good enough. Instead we advocate TOA-based algorithms that can actively mitigate errors due to NLOS.

A Robust Detection and Optimization Approach for Delayed Measurements in UWB Particle-Filter-Based Indoor Positioning

“…Various methods have been proposed for TOA-based positioning using particle filters. Savic and Larsson (2016) proposed an improved particle-filter-based positioning algorithm with a Gaussian process regression (GPR) based machine-learning method for correcting delayed range measurements. The positioning accuracy of the algorithm is highly dependent on the environment of the collected training data, and thus it has poor robustness to changes in a given positioning environment.…”

“…A simple and straightforward way to do this is to remove the identified delayed measurements and evaluate the predicted particles using the remaining identified direct range measurements. However, Savic and Larsson (2016) point out that the particle filter using the range measurements with the rejection of identified delayed measurements may still suffer from serious accuracy degeneration due to the accumulation of positioning error over time.…”

A Robust Localization, Slip Estimation, and Compensation System for WMR in the Indoor Environments

“…One best Bayesian filtering based candidate to manage measurement uncertainty is Particle Filtering (PF). It is able to manage non-Gaussian uncertainties that typically appear in the presence of NLOS conditions [35,36], it is used for solving localization, tracking, and navigation problems [37]. PF is a Monte Carlo estimation algorithm in which a posteriori probability density function is constructed by using weighted particles to make it suitable for the state estimation of non-Gaussian systems [38].…”