Multisensor Covariance Intersection Fusion Kalman Filters

Abstract:

For multisensor system with colored measurement noises, the common disturbance noises and measurement biases, the batch covariance intersection fusion (BCI) Kalman filter and the sequential covariance intersection fusion (SCI) Kalman filter are presented, which can avoid the computation of the local filtering errors and reduce the computational burden significantly. Under the linear unbiased minimum variance (ULMV) criterion, the three weighted fusion Kalman filters (weighted by matrices, scalars or diagonal matrices) are also presented. Their accuracy relations are analyzed and compared. Specially, the accuracy of the proposed covariance intersection fusion Kalman filters are higher than that of each local Kalman filters, and is lower than that of optimal fuser weighted by matrices. The geometric interpretation of the accuracy relations is given by the covariance ellipses. A Monte-Carlo simulation example for a tracking system verifies the correctness of the theoretical accuracy relations.

• Periodical: Applied Mechanics and Materials (Volumes 373-375)
• Pages: 946-952
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.373-375.946
• Online since: August 2013
• Authors: Wen Juan Qi, Peng Zhang, Zi Li Deng, Yuan Gao
• Keywords: Batch Covariance Intersection Fusion, Covariance Ellipse, Multisensor Information Fusion, Sequential Covariance Intersection Fusion, Weighted Fusion

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