Characterization of Non−Line−of−Sight (NLOS) Bias via Analysis of Clutter Topology

Abstract

Clutter-prone environments are challenging for range-based localization, where distances between references and the unlocalized node are estimated using wireless technologies like radio, ultrasound, etc. This is so due to the incidence of Non-Line-Of-Sight (NLOS) distance measurements as the direct path between the twp is occluded by the presence of clutter. Thus NLOS distances, having large positive biases, can severely degrade localization accuracy. Till date, NLOS error has been modeled as various distributions including uniform, Gaussian, Poisson and exponential. In this paper, we show the clutter topology itself plays a vital role in the characterization of NLOS bias. We enumerate a feature-set for clutter topologies, including features that can be practically deduced, and analyze the significance of these features on NLOS characterization. We then analyze their effects on the estimation of the NLOS incidence probability as well as the NLOS bias distribution for arbitrary clutter topologies.