A Weighted Measurement Fusion Particle Filter for Nonlinear Multisensory Systems Based on Gauss–Hermite Approximation

We addressed the fusion estimation problem for nonlinear multisensory systems. Based on the Gauss–Hermite approximation and weighted least square criterion, an augmented high-dimension measurement from all sensors was compressed into a lower dimension. By combining the low-dimension measurement func...

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Detalles Bibliográficos
Autores principales: Li, Yun, Sun, Shu Li, Hao, Gang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2017
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5677455/
https://www.ncbi.nlm.nih.gov/pubmed/28956862
http://dx.doi.org/10.3390/s17102222
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author Li, Yun
Sun, Shu Li
Hao, Gang
author_facet Li, Yun
Sun, Shu Li
Hao, Gang
author_sort Li, Yun
collection PubMed
description We addressed the fusion estimation problem for nonlinear multisensory systems. Based on the Gauss–Hermite approximation and weighted least square criterion, an augmented high-dimension measurement from all sensors was compressed into a lower dimension. By combining the low-dimension measurement function with the particle filter (PF), a weighted measurement fusion PF (WMF-PF) is presented. The accuracy of WMF-PF appears good and has a lower computational cost when compared to centralized fusion PF (CF-PF). An example is given to show the effectiveness of the proposed algorithms.
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spelling pubmed-56774552017-11-17 A Weighted Measurement Fusion Particle Filter for Nonlinear Multisensory Systems Based on Gauss–Hermite Approximation Li, Yun Sun, Shu Li Hao, Gang Sensors (Basel) Article We addressed the fusion estimation problem for nonlinear multisensory systems. Based on the Gauss–Hermite approximation and weighted least square criterion, an augmented high-dimension measurement from all sensors was compressed into a lower dimension. By combining the low-dimension measurement function with the particle filter (PF), a weighted measurement fusion PF (WMF-PF) is presented. The accuracy of WMF-PF appears good and has a lower computational cost when compared to centralized fusion PF (CF-PF). An example is given to show the effectiveness of the proposed algorithms. MDPI 2017-09-28 /pmc/articles/PMC5677455/ /pubmed/28956862 http://dx.doi.org/10.3390/s17102222 Text en © 2017 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Li, Yun
Sun, Shu Li
Hao, Gang
A Weighted Measurement Fusion Particle Filter for Nonlinear Multisensory Systems Based on Gauss–Hermite Approximation
title A Weighted Measurement Fusion Particle Filter for Nonlinear Multisensory Systems Based on Gauss–Hermite Approximation
title_full A Weighted Measurement Fusion Particle Filter for Nonlinear Multisensory Systems Based on Gauss–Hermite Approximation
title_fullStr A Weighted Measurement Fusion Particle Filter for Nonlinear Multisensory Systems Based on Gauss–Hermite Approximation
title_full_unstemmed A Weighted Measurement Fusion Particle Filter for Nonlinear Multisensory Systems Based on Gauss–Hermite Approximation
title_short A Weighted Measurement Fusion Particle Filter for Nonlinear Multisensory Systems Based on Gauss–Hermite Approximation
title_sort weighted measurement fusion particle filter for nonlinear multisensory systems based on gauss–hermite approximation
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5677455/
https://www.ncbi.nlm.nih.gov/pubmed/28956862
http://dx.doi.org/10.3390/s17102222
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