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

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

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...

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Bibliographic Details
Main Authors: Yuan Yang, Manyi Wang, Yunxia Qiao, Bo Zhang, Haoran Yang
Format: Article
Language:English
Published: MDPI AG 2020-11-01
Series:Remote Sensing
Subjects:
indoor localization
time-of-flight ranging
Bayesian smoothing
nonlinear non-Gaussian models
Sequential Monte Carlo
Online Access:https://www.mdpi.com/2072-4292/12/22/3838

Internet

https://www.mdpi.com/2072-4292/12/22/3838

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