Abstract
One of the main challenges in indoor time-of-arrival (TOA)-based wireless localization systems is to mitigate non-line-of-sight (NLOS) propagation conditions, which degrade the overall positioning performance. The positive skewed non-Gaussian nature of TOA observations under LOS/NLOS conditions can be modeled as a heavy-tailed skew t-distributed measurement noise. The main goal of this article is to provide a robust Bayesian inference framework to deal with target localization under NLOS conditions. A key point is to take advantage of the conditionally Gaussian formulation of the skew t-distribution, thus being able to use computationally light Gaussian filtering and smoothing methods as the core of the new approach. The unknown non-Gaussian noise latent variables are marginalized using Monte Carlo sampling. Numerical results are provided to show the performance improvement of the proposed approach.
Highlights
The knowledge of position is ubiquitous in many applications and services, playing an important role
The article is organized as follows: first, we provide a discussion on Gaussian filtering and smoothing in nonlinear/Gaussian systems, together with the sigma-pointbased approximation of the multidimensional integrals in the conceptual solution, being computationally more efficient than sequential Monte Carlo (SMC) methods under the Gaussian assumption; we provide the conditionally Gaussian formulation of the measurement noise and a method to deal with the unknown non-Gaussian noise latent variables, and we propose a NLOS indoor localization solution, based on the Gaussian smoother and the sequential noise latent variables marginalization
We propose to use square-root cubature and quadrature Kalman filters/smoothers [38, 43] as the core implementation of the new square-root Monte Carlo sigma-point Gaussian filter and smoother (MSPGF/S)
Summary
The knowledge of position is ubiquitous in many applications and services, playing an important role. It is important to point out that (i) these contributions deal with either nonlinear systems corrupted by symmetric distributed noises or linear SSMs under skewed noise and (ii) the core of these methods use standard Bayesian filtering algorithms, the smoothing problem needs to be further analyzed within this context. The article is organized as follows: first, we provide a discussion on Gaussian filtering and smoothing in nonlinear/Gaussian systems, together with the sigma-pointbased approximation of the multidimensional integrals in the conceptual solution, being computationally more efficient than SMC methods under the Gaussian assumption; we provide the conditionally Gaussian formulation of the measurement noise and a method to deal with the unknown non-Gaussian noise latent variables, and we propose a NLOS indoor localization solution, based on the Gaussian smoother and the sequential noise latent variables marginalization.
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