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A Framework of Covariance Projection on Constraint Manifold for Data Fusion †
A general framework of data fusion is presented based on projecting the probability distribution of true states and measurements around the predicted states and actual measurements onto the constraint manifold. The constraint manifold represents the constraints to be satisfied among true states and...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5981452/ https://www.ncbi.nlm.nih.gov/pubmed/29772850 http://dx.doi.org/10.3390/s18051610 |
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author | Abu Bakr, Muhammad Lee, Sukhan |
author_facet | Abu Bakr, Muhammad Lee, Sukhan |
author_sort | Abu Bakr, Muhammad |
collection | PubMed |
description | A general framework of data fusion is presented based on projecting the probability distribution of true states and measurements around the predicted states and actual measurements onto the constraint manifold. The constraint manifold represents the constraints to be satisfied among true states and measurements, which is defined in the extended space with all the redundant sources of data such as state predictions and measurements considered as independent variables. By the general framework, we mean that it is able to fuse any correlated data sources while directly incorporating constraints and identifying inconsistent data without any prior information. The proposed method, referred to here as the Covariance Projection (CP) method, provides an unbiased and optimal solution in the sense of minimum mean square error (MMSE), if the projection is based on the minimum weighted distance on the constraint manifold. The proposed method not only offers a generalization of the conventional formula for handling constraints and data inconsistency, but also provides a new insight into data fusion in terms of a geometric-algebraic point of view. Simulation results are provided to show the effectiveness of the proposed method in handling constraints and data inconsistency. |
format | Online Article Text |
id | pubmed-5981452 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-59814522018-06-05 A Framework of Covariance Projection on Constraint Manifold for Data Fusion † Abu Bakr, Muhammad Lee, Sukhan Sensors (Basel) Article A general framework of data fusion is presented based on projecting the probability distribution of true states and measurements around the predicted states and actual measurements onto the constraint manifold. The constraint manifold represents the constraints to be satisfied among true states and measurements, which is defined in the extended space with all the redundant sources of data such as state predictions and measurements considered as independent variables. By the general framework, we mean that it is able to fuse any correlated data sources while directly incorporating constraints and identifying inconsistent data without any prior information. The proposed method, referred to here as the Covariance Projection (CP) method, provides an unbiased and optimal solution in the sense of minimum mean square error (MMSE), if the projection is based on the minimum weighted distance on the constraint manifold. The proposed method not only offers a generalization of the conventional formula for handling constraints and data inconsistency, but also provides a new insight into data fusion in terms of a geometric-algebraic point of view. Simulation results are provided to show the effectiveness of the proposed method in handling constraints and data inconsistency. MDPI 2018-05-17 /pmc/articles/PMC5981452/ /pubmed/29772850 http://dx.doi.org/10.3390/s18051610 Text en © 2018 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 Abu Bakr, Muhammad Lee, Sukhan A Framework of Covariance Projection on Constraint Manifold for Data Fusion † |
title | A Framework of Covariance Projection on Constraint Manifold for Data Fusion † |
title_full | A Framework of Covariance Projection on Constraint Manifold for Data Fusion † |
title_fullStr | A Framework of Covariance Projection on Constraint Manifold for Data Fusion † |
title_full_unstemmed | A Framework of Covariance Projection on Constraint Manifold for Data Fusion † |
title_short | A Framework of Covariance Projection on Constraint Manifold for Data Fusion † |
title_sort | framework of covariance projection on constraint manifold for data fusion † |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5981452/ https://www.ncbi.nlm.nih.gov/pubmed/29772850 http://dx.doi.org/10.3390/s18051610 |
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