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NLOS mitigation in indoor localization by marginalized Monte Carlo Gaussian smoothing

Vilà-Valls, Jordi and Closas, Pau NLOS mitigation in indoor localization by marginalized Monte Carlo Gaussian smoothing. (2017) EURASIP Journal on Advances in Signal Processing, 2017 (1). ISSN 1687-6172

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Official URL: https://doi.org/10.1186/s13634-017-0498-4

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.

Item Type:Article
Audience (journal):International peer-reviewed journal
Uncontrolled Keywords:
Institution:Other partners > Centre Tecnològic de Telecomunicacions de Catalunya - CTTC (SPAIN)
Other partners > Northeastern University (USA)
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Deposited On:19 Apr 2021 14:28

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