A Minimax Unbiased Estimation Fusion in Distributed Multisensor Localization and Tracking

A minimax estimation fusion in distributed multisensor systems is proposed, which aims to minimize the worst-case squared estimation error when the cross-covariances between local sensors are unknown and the normalized estimation errors of local sensors are norm bounded. The proposed estimation fusi...

Full description

Bibliographic Details
Main Authors: Xiaomei Qu, Jie Zhou
Format: Article
Language:English
Published: Hindawi - SAGE Publishing 2012-12-01
Series:International Journal of Distributed Sensor Networks
Online Access:https://doi.org/10.1155/2012/294578
_version_ 1797766480963567616
author Xiaomei Qu
Jie Zhou
author_facet Xiaomei Qu
Jie Zhou
author_sort Xiaomei Qu
collection DOAJ
description A minimax estimation fusion in distributed multisensor systems is proposed, which aims to minimize the worst-case squared estimation error when the cross-covariances between local sensors are unknown and the normalized estimation errors of local sensors are norm bounded. The proposed estimation fusion is called as the Chebyshev fusion estimation (CFE) because its geometrical interpretation is in coincidence with the Chebyshev center, which is a nonlinear combination of local estimates. Theoretically, the CFE is better than any local estimator in the sense of the worst-case squared estimation error and is robust to the choice of the supporting bound. The simulation results illustrate that the proposed CFE is a robust fusion in localization and tracking and more accurate than the previous covariance intersection (CI) method.
first_indexed 2024-03-12T20:25:52Z
format Article
id doaj.art-eaedbc51051a4c82922bac6030c6f3ed
institution Directory Open Access Journal
issn 1550-1477
language English
last_indexed 2024-03-12T20:25:52Z
publishDate 2012-12-01
publisher Hindawi - SAGE Publishing
record_format Article
series International Journal of Distributed Sensor Networks
spelling doaj.art-eaedbc51051a4c82922bac6030c6f3ed2023-08-02T00:33:29ZengHindawi - SAGE PublishingInternational Journal of Distributed Sensor Networks1550-14772012-12-01810.1155/2012/294578A Minimax Unbiased Estimation Fusion in Distributed Multisensor Localization and TrackingXiaomei Qu0Jie Zhou1 College of Computer Science and Technology, Southwest University for Nationalities, Chengdu, Sichuan 610041, China College of Mathematics, Sichuan University, Chengdu, Sichuan 610064, ChinaA minimax estimation fusion in distributed multisensor systems is proposed, which aims to minimize the worst-case squared estimation error when the cross-covariances between local sensors are unknown and the normalized estimation errors of local sensors are norm bounded. The proposed estimation fusion is called as the Chebyshev fusion estimation (CFE) because its geometrical interpretation is in coincidence with the Chebyshev center, which is a nonlinear combination of local estimates. Theoretically, the CFE is better than any local estimator in the sense of the worst-case squared estimation error and is robust to the choice of the supporting bound. The simulation results illustrate that the proposed CFE is a robust fusion in localization and tracking and more accurate than the previous covariance intersection (CI) method.https://doi.org/10.1155/2012/294578
spellingShingle Xiaomei Qu
Jie Zhou
A Minimax Unbiased Estimation Fusion in Distributed Multisensor Localization and Tracking
International Journal of Distributed Sensor Networks
title A Minimax Unbiased Estimation Fusion in Distributed Multisensor Localization and Tracking
title_full A Minimax Unbiased Estimation Fusion in Distributed Multisensor Localization and Tracking
title_fullStr A Minimax Unbiased Estimation Fusion in Distributed Multisensor Localization and Tracking
title_full_unstemmed A Minimax Unbiased Estimation Fusion in Distributed Multisensor Localization and Tracking
title_short A Minimax Unbiased Estimation Fusion in Distributed Multisensor Localization and Tracking
title_sort minimax unbiased estimation fusion in distributed multisensor localization and tracking
url https://doi.org/10.1155/2012/294578
work_keys_str_mv AT xiaomeiqu aminimaxunbiasedestimationfusionindistributedmultisensorlocalizationandtracking
AT jiezhou aminimaxunbiasedestimationfusionindistributedmultisensorlocalizationandtracking
AT xiaomeiqu minimaxunbiasedestimationfusionindistributedmultisensorlocalizationandtracking
AT jiezhou minimaxunbiasedestimationfusionindistributedmultisensorlocalizationandtracking