Joint tracking of manoeuvring targets and classification of their manoeuvrability

Authors:
Simon Maskell
Affiliations:
QinetiQ Ltd., Malvern, Worcestershire, UK and Department of Engineering, University of Cambridge, Cambridge, UK
Venue:
EURASIP Journal on Applied Signal Processing
Year:
2004

Citing 2
Cited 3

A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking

IEEE Transactions on Signal Processing
The PDF projection theorem and the class-specific method

IEEE Transactions on Signal Processing

A Bayesian approach to fusing uncertain, imprecise and conflicting information

Information Fusion
A Bayesian approach to joint tracking and identification of geometric shapes in video sequences

Image and Vision Computing
Multiple model target tracking with variable rate particle filters

Digital Signal Processing

Quantified Score

Hi-index	0.00

Visualization

Abstract

Semi-Markov models are a generalisation of Markov models that explicitly model the state-dependent sojourn time distribution, the time for which the system remains in a given state. Markov models result in an exponentially distributed sojourn time, while semi-Markov models make it possible to define the distribution explicitly. Such models can be used to describe the behaviour of manoeuvring targets, and particle filtering can then facilitate tracking. An architecture is proposed that enables particle filters to be both robust and efficient when conducting joint tracking and classification. It is demonstrated that this approach can be used to classify targets on the basis of their manoeuvrability.