Comparison of two measurement fusion methods for Kalman-filter-based multisensor data fusion

Author

Gan, Qiaoqiang ; Harris, Chris J.

Author_Institution

Dept. of Electron. & Comput. Sci., Southampton Univ.

Volume

37

Issue

1

fYear

2001

fDate

1/1/2001 12:00:00 AM

Firstpage

273

Lastpage

279

Abstract

Currently there exist two commonly used measurement fusion methods for Kalman-filter-based multisensor data fusion. The first (Method I) simply merges the multisensor data through the observation vector of the Kalman filter, whereas the second (Method II) combines the multisensor data based on a minimum-mean-square-error criterion. This paper, based on an analysis of the fused state estimate covariances of the two measurement fusion methods, shows that the two measurement fusion methods are functionally equivalent if the sensors used for data fusion, with different and independent noise characteristics, have identical measurement matrices. Also presented are simulation results on state estimation using the two measurement fusion methods, followed by the analysis of the computational advantages of each method

Keywords

Kalman filters; covariance matrices; least mean squares methods; sensor fusion; state estimation; tracking; Kalman-filter; data fusion; fused state estimate covariances; measurement fusion methods; measurement matrices; minimum-mean-square-error criterion; multisensor data fusion; noise characteristics; simulation results; state estimation; Aerodynamics; Computational efficiency; Computational modeling; Covariance matrix; Current measurement; Noise measurement; Sensor fusion; Sensor phenomena and characterization; State estimation; Target tracking;

fLanguage

English

Journal_Title

Aerospace and Electronic Systems, IEEE Transactions on

Publisher

ieee

ISSN

0018-9251

Type

jour

DOI

10.1109/7.913685

Filename

913685