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International Journal of Distributed Sensor Networks
Volume 2012 (2012), Article ID 294578, 8 pages
http://dx.doi.org/10.1155/2012/294578
Research Article

A Minimax Unbiased Estimation Fusion in Distributed Multisensor Localization and Tracking

1College of Computer Science and Technology, Southwest University for Nationalities, Chengdu, Sichuan 610041, China
2College of Mathematics, Sichuan University, Chengdu, Sichuan 610064, China

Received 30 August 2012; Accepted 26 October 2012

Academic Editor: Long Cheng

Copyright © 2012 Xiaomei Qu and Jie Zhou. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

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.