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
fDate :
Oct. 29 2000-Nov. 1 2000
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;
Conference_Titel :
Signals, Systems and Computers, 2000. Conference Record of the Thirty-Fourth Asilomar Conference on
Conference_Location :
Pacific Grove, CA, USA
Print_ISBN :
0-7803-6514-3
DOI :
10.1109/ACSSC.2000.911288
