Weighted Measurement Fusion Kalman Filter for Multisensor Descriptor System

Yin Feng Dou; Wen Juan Qi; Chen Jian Ran

doi:10.4028/www.scientific.net/AMM.373-375.940

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 373-375Weighted Measurement Fusion Kalman Filter for...

Weighted Measurement Fusion Kalman Filter for Multisensor Descriptor System

Abstract:

For the multi-sensor descriptor system with correlated measurement noises and same measurement matrix, the reduced-order sub-systems are obtained, applying singular value decomposition method. And measurements of every sensor are transformed to the measurement of one state component. For this new reduced-order normal system, the new fused measurement can be obtained applying the weighted least squares method. Then, the weighted measurement fusion Kalman filter and its filtering error variance are presented, applying a single Kalman filter. This method avoids computing the cross-variances among all local filters, compared with the state fusion Kalman filtering algorithm. And the accuracy of this fused filter is higher than that of local filter and state fusion Kalman filter. A simulation example verifies its effectiveness.

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Periodical:

Applied Mechanics and Materials (Volumes 373-375)

Pages:

940-945

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.373-375.940

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Online since:

August 2013

Authors:

Yin Feng Dou, Wen Juan Qi, Chen Jian Ran

Keywords:

Descriptor System, Kalman Filter (KF), Multisensor Fusion

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