Some remarks on Kalman filters for the multisensor fusion
Some remarks on Kalman filters for the multisensor fusion
Multisensor data fusion has found widespread application in industry and commerce. The purpose of data fusion is to produce an improved model or estimate of a system from a set of independent data sources. There are various multisensor data fusion approaches, of which Kalman filtering is one of the most significant. Methods for Kalman filter based data fusion includes measurement fusion and state fusion. This paper gives first a simple a review of both measurement fusion and state fusion, and secondly proposes two new methods of state fusion based on fusion procedures at the prediction and update level, respectively, of the Kalman filter. The theoretical derivation for these algorithms are derived. To illustrate their application, a simple example is performed to evaluate the proposed methods and compare their performance with the conventional state fusion method and measurement fusion methods.
191-201
Gao, J.B.
5adc3f26-6fe2-4b31-9d1a-d1c64b7eefe0
Harris, C.J.
c4fd3763-7b3f-4db1-9ca3-5501080f797a
September 2002
Gao, J.B.
5adc3f26-6fe2-4b31-9d1a-d1c64b7eefe0
Harris, C.J.
c4fd3763-7b3f-4db1-9ca3-5501080f797a
Abstract
Multisensor data fusion has found widespread application in industry and commerce. The purpose of data fusion is to produce an improved model or estimate of a system from a set of independent data sources. There are various multisensor data fusion approaches, of which Kalman filtering is one of the most significant. Methods for Kalman filter based data fusion includes measurement fusion and state fusion. This paper gives first a simple a review of both measurement fusion and state fusion, and secondly proposes two new methods of state fusion based on fusion procedures at the prediction and update level, respectively, of the Kalman filter. The theoretical derivation for these algorithms are derived. To illustrate their application, a simple example is performed to evaluate the proposed methods and compare their performance with the conventional state fusion method and measurement fusion methods.
This record has no associated files available for download.
More information
Accepted/In Press date: 26 March 2002
e-pub ahead of print date: 14 August 2002
Published date: September 2002
Organisations:
Southampton Wireless Group
Identifiers
Local EPrints ID: 251191
URI: http://eprints.soton.ac.uk/id/eprint/251191
PURE UUID: db4dfa8c-ac72-4ff5-9a16-ccdeef3027b7
Catalogue record
Date deposited: 04 Jun 2001
Last modified: 14 Mar 2024 05:10
Export record
Altmetrics
Contributors
Author:
J.B. Gao
Author:
C.J. Harris
Download statistics
Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.
View more statistics