Characterization of non-line-of-sight (NLOS) bias via analysis of clutter topology

Clutter-prone environments are challenging for range-based localization, where distances between anchors and the unlocalised 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 two 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 modelled as various distributions including uniform, Gaussian, Poisson and exponential. In this paper, we show that 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 without complete knowledge of the clutter topology. We then analyze the significance of these features, both individually and in combination with each other, in the estimation of the NLOS rate as well as the NLOS bias distribution for arbitrary clutter topologies. We show that we can obtain the NLOS rate with an error of only 0.03 for a given clutter topology using only those clutter topology features that can be practically realized in a real deployment. We show that estimating the NLOS bias distribution is more challenging which give a small number of poor estimations.