NLOS mitigation in indoor localization by marginalized Monte Carlo Gaussian smoothing

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 condit...

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Detalhes bibliográficos
Main Authors: Jordi Vilà-Valls, Pau Closas
Formato: Artigo
Idioma:Inglês
Publicado em: SpringerOpen 2017-08-01
Colecção:EURASIP Journal on Advances in Signal Processing
Assuntos:
Robust Bayesian inference
Gaussian filtering and smoothing
NLOS mitigation
Skew t-distributed measurement noise
Indoor localization
Monte Carlo integration
Acesso em linha:http://link.springer.com/article/10.1186/s13634-017-0498-4
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