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Sensors 2016, 16(5), 692; doi:10.3390/s16050692

A Nonlinear Framework of Delayed Particle Smoothing Method for Vehicle Localization under Non-Gaussian Environment

1
College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, China
2
State Key Laboratory of Integrated Service Networks, Xidian University, Xi’an 710071, China
3
Department of Applied Sciences, Ecole Normale Supérieure, 6983 Bujumbura, Burundi
*
Author to whom correspondence should be addressed.
Academic Editor: Lyudmila Mihaylova
Received: 20 February 2016 / Revised: 5 May 2016 / Accepted: 6 May 2016 / Published: 13 May 2016
(This article belongs to the Special Issue Scalable Localization in Wireless Sensor Networks)
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Abstract

In this paper, a novel nonlinear framework of smoothing method, non-Gaussian delayed particle smoother (nGDPS), is proposed, which enables vehicle state estimation (VSE) with high accuracy taking into account the non-Gaussianity of the measurement and process noises. Within the proposed method, the multivariate Student’s t-distribution is adopted in order to compute the probability distribution function (PDF) related to the process and measurement noises, which are assumed to be non-Gaussian distributed. A computation approach based on Ensemble Kalman Filter (EnKF) is designed to cope with the mean and the covariance matrix of the proposal non-Gaussian distribution. A delayed Gibbs sampling algorithm, which incorporates smoothing of the sampled trajectories over a fixed-delay, is proposed to deal with the sample degeneracy of particles. The performance is investigated based on the real-world data, which is collected by low-cost on-board vehicle sensors. The comparison study based on the real-world experiments and the statistical analysis demonstrates that the proposed nGDPS has significant improvement on the vehicle state accuracy and outperforms the existing filtering and smoothing methods. View Full-Text
Keywords: particle filter; fixed-delay smoothing; non-Gaussian noise; Ensemble Kalman Filter; vehicle localization particle filter; fixed-delay smoothing; non-Gaussian noise; Ensemble Kalman Filter; vehicle localization
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Xiao, Z.; Havyarimana, V.; Li, T.; Wang, D. A Nonlinear Framework of Delayed Particle Smoothing Method for Vehicle Localization under Non-Gaussian Environment. Sensors 2016, 16, 692.

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