Title :
A hybrid bootstrap filter for target tracking in clutter
Author_Institution :
Defence Res. Agency, Farnborough, UK
Abstract :
The problem of tracking multiple targets with multiple sensors in the presence of interfering measurements is considered. A new hybrid bootstrap filter is proposed. The bootstrap filter is an approach where random samples are used to represent the target posterior distributions. By using this approach, the author circumvents the usual problem of an exponentially increasing number of association hypotheses as well as allowing the use of any nonlinear/non-Gaussian system and/or measurement models
Keywords :
Monte Carlo methods; clutter; filtering theory; sensor fusion; state estimation; target tracking; clutter; hybrid bootstrap filter; measurement models; multiple sensors; multiple targets; nonlinear/nonGaussian system models; posterior distributions; random samples; target tracking; Bayesian methods; Clutter; Filters; Information filtering; Interference; Merging; Noise measurement; Probability density function; Sensor systems; Target tracking;
Conference_Titel :
American Control Conference, Proceedings of the 1995
Conference_Location :
Seattle, WA
Print_ISBN :
0-7803-2445-5
DOI :
10.1109/ACC.1995.529326
