DocumentCode
2964343
Title
Data fusion using multiple models
Author
Sworder, D.D. ; Boyd, J.E. ; Eliott, R.J. ; Hutchins, R.G.
Author_Institution
Dept. of Electr. & Comput. Eng., California Univ., San Diego, La Jolla, CA, USA
Volume
2
fYear
2000
fDate
Oct. 29 2000-Nov. 1 2000
Firstpage
1749
Abstract
Multiple model fusion is useful in applications in which the model of the signal processes is not known with certainty. This paper compares two current fusion algorithms with a novel alternative. The new fusion approach is shown to give improved performance when the observation rate is slow as compared with the important time constants of the signal.
Keywords
Gaussian processes; Kalman filters; filtering theory; image enhancement; parameter estimation; sensor fusion; wavelet transforms; Gaussian wavelet estimator; Kalman filter bank; data fusion algorithms; image enhanced IMM; interacting multiple model estimator; linear filters; maneuvering aircraft tracking; multiple models; observation rate; performance; signal process model; signal time constants; Application software; Costs; Filter bank; Mathematical model; Nonlinear filters; Signal processing; Signal processing algorithms; State estimation; State-space methods; Time measurement;
fLanguage
English
Publisher
ieee
Conference_Titel
Signals, Systems and Computers, 2000. Conference Record of the Thirty-Fourth Asilomar Conference on
Conference_Location
Pacific Grove, CA, USA
ISSN
1058-6393
Print_ISBN
0-7803-6514-3
Type
conf
DOI
10.1109/ACSSC.2000.911288
Filename
911288
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