Modeling Distance and Bandwidth Dependency of TOA‐Based UWB Ranging Error for Positioning

Abstract

A statistical model for the range error provided by TOA estimation using UWB signals is given, based on UWB channel measurements between 3.1 and 10.6 GHz. The range error has been modeled as a Gaussian random variable for LOS and as a combination of a Gaussian and an exponential random variable for NLOS. The distance and bandwidth dependency of both the mean and the standard deviation of the range error has been analyzed, and insight is given in the different phenomena which affect the estimation accuracy. A possible application of the model to weighted least squares positioning is finally investigated. Noticeable improvements compared to the traditional least squares method have been obtained.

An assessment on the use of stationary vehicles to support cooperative positioning systems

“…• EKF [23]: -Q k−1 = σ 2 Q I, R k = σ 2 R I, Γ k−1 = σ 2 Γ I, with I the identity matrix with proper dimensions, and with σ Γ = 0.5, σ Q = 2 and σ R as in general assumptions. All the previous assumptions, and the choice of parameters, are consistent with those used in [19], [22], [23], where σ R = 0.2 corresponds to UBW-based ranging and σ R = 4 corresponds to radio-based ranging.…”

“…Note that despite the fact that radio ranging techniques cannot provide accurate inter-vehicle distance measurements for CP purposes, simulation results have shown that their utility for the localisation of moving vehicles can be potentially greatly improved using the suggested approach, even in the case of simplistic and imprecise CP algorithms. In our experiments we used a short-range communication distance of 15 m, mainly for mitigating the effects of shadowing and multipath (see references [19], [20]). Clearly, the results for the large scale scenario can be improved if an extended communication range is adopted (as preliminary concluded in Section V-B), but, as we have already mentioned, in such a case the effects of shadowing and multipath cannot be neglected.…”

Bayesian filtering for indoor localization and tracking in wireless sensor networks

“…The latter includes multipath effect (bias) and LOS/NLOS conditions. Recent works have reinforced this idea [23,24].…”

Distributed Formation Estimation Via Pairwise Distance Measurements

“…The relative distance d ij between the two agents i and j can be obtained from the range sensor. Following [22], we model uncertainty of the measurement with Gaussian noise:…”