12,000

We have over 12,000 students, from over 100 countries, within one of the safest campuses in the UK

93%

93% of Lancaster students go into work or further study within six months of graduating

Home > Research > Publications & Outputs > Extended object tracking with convolution parti...
View graph of relations

« Back

Extended object tracking with convolution particle filtering

Research output: Contribution in Book/Report/ProceedingsPaper

Published

Publication date1/09/2012
Host publicationIntelligent Systems (IS), 2012 6th IEEE International Conference
PublisherIEEE
Pages96-101
Number of pages6
ISBN (Electronic)978-1-4673-2277-5
ISBN (Print)978-1-4673-2276-8
Original languageEnglish

Abstract

This paper proposes a sequential Monte Carlo filter (particle filter) for state and parameter estimation of dynamic systems. It is applied to the problem of extended object tracking in the presence of dense clutter. The unknown length of a stick-shape object is estimated in addition to the kinematic parameters. The kernel density estimation technique is utilised to approximate the joint posterior density of target state and static size parameters. The convolution particle filtering approach is validated on a Poisson model for the measurements, originating from the target and clutter. Examples illustrating the filter performance are presented. Simulation results show that the convolution particle filter provides accurate on-line tracking, with very good estimates both for the target kinematic states and for the parameters of the target extent.