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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(Document in English)
PDF (Publisher's version) - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader 738kB |
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 |
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Audience (journal): | International peer-reviewed journal |
Uncontrolled Keywords: | |
Institution: | Other partners > Centre Tecnològic de Telecomunicacions de Catalunya - CTTC (SPAIN) Other partners > Northeastern University (USA) |
Statistics: | download |
Deposited On: | 19 Apr 2021 14:28 |
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