DocumentCode
353921
Title
Stochastic estimation using a continuum of models
Author
Layne, Jeffery ; Weaver, Scott
Author_Institution
Wright Res. & Dev. Center, Wright-Patterson AFB, OH, USA
Volume
1
fYear
2000
fDate
10-13 July 2000
Abstract
We investigate a recursive multiple model tracking approach similar to the Generalized Pseudo-Bayesian 1 (GPB1) (Bar-Shalom and Li, 1993) approach. However, we consider a continuum of models rather than the discrete set that is usually implemented in the GPBI method. By doing so better models are available to improve tracker performance and solve the symmetry problem inherent in most multiple model approaches.
Keywords
Kalman filters; parameter estimation; probability; sensor fusion; symmetry; target tracking; Generalized Pseudo-Bayesian; Kalman filtering; continuum of models; multiple model estimation; recursive multiple model tracking; stochastic estimation; symmetry problem; target tracking; Acceleration; Adaptation model; Aerospace electronics; Filtering; Kalman filters; Predictive models; Stochastic processes; Target tracking; Uncertainty; Weight measurement;
fLanguage
English
Publisher
ieee
Conference_Titel
Information Fusion, 2000. FUSION 2000. Proceedings of the Third International Conference on
Conference_Location
Paris, France
Print_ISBN
2-7257-0000-0
Type
conf
DOI
10.1109/IFIC.2000.862660
Filename
862660
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