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Efficient Marginalized Particle Smoother for Indoor CSS–TOF Localization with Non-Gaussian Errors

Authors :
Yuan Yang
Manyi Wang
Yunxia Qiao
Bo Zhang
Haoran Yang
Source :
Remote Sensing, Vol 12, Iss 22, p 3838 (2020)
Publication Year :
2020
Publisher :
MDPI AG, 2020.

Abstract

The time-series state and parameter estimations of indoor localization continue to be a topic of growing importance. To deal with the nonlinear and positive skewed non-Gaussian dynamic of indoor CSS–TOF (Chirp-Spread-Spectrum Time-of-Flight) ranging measurements and position estimations, Monte Carlo Bayesian smoothers are promising as involving the past, present, and future observations. However, the main problems are how to derive trackable smoothing recursions and to avoid the degeneracy of particle-based smoothed distributions. To incorporate the backward smoothing density propagation with the forward probability recursion efficiently, we propose a lightweight Marginalized Particle Smoother (MPS) for nonlinear and non-Gaussian errors mitigation. The performance of the position prediction, filtering, and smoothing are investigated in real-world experiments carried out with vehicle on-board sensors. Results demonstrate the proposed smoother enables a great tool by reducing temporal and spatial errors of mobile trajectories, with the cost of a few sequence delay and a small number of particles. Therefore, MPS outperforms the filtering and smoothing methods under weak assumptions, low computation, and memory requirements. In the view that the sampled trajectories stay numerically stable, the MPS form is validated to be applicable for time-series position tracking.

Details

Language :
English
ISSN :
20724292
Volume :
12
Issue :
22
Database :
Directory of Open Access Journals
Journal :
Remote Sensing
Publication Type :
Academic Journal
Accession number :
edsdoj.99e24481f35e4db7a5242601a9f44f35
Document Type :
article
Full Text :
https://doi.org/10.3390/rs12223838