Title :
Multiple model bootstrap filter for maneuvering target tracking
Author :
McGinnity, Shaun ; Irwin, George W.
Author_Institution :
Dept. of Electr. & Electron. Eng., Queen´´s Univ., Belfast, UK
fDate :
7/1/2000 12:00:00 AM
Abstract :
The extension of the bootstrap filter to the multiple model target tracking problem is considered. Bayesian bootstrap filtering is a very powerful technique since it represents samples by random samples and is therefore not restricted to linear, Gaussian systems, making it ideal for the multiple model problem where very complex densities can be generated
Keywords :
Bayes methods; Markov processes; bootstrap circuits; nonlinear systems; parameter estimation; probability; target tracking; Bayesian bootstrap filtering; Markov model; complex densities; linear Gaussian systems; maneuvering target tracking; multiple model bootstrap filter; random samples; Bayesian methods; Density functional theory; Matched filters; Merging; Power generation; Power system modeling; State estimation; State-space methods; Switches; Target tracking;
Journal_Title :
Aerospace and Electronic Systems, IEEE Transactions on
