An improved particle filter for non-linear problems

Carpenter, J and Clifford, Peter and Fearnhead, Paul (1999) An improved particle filter for non-linear problems. IEE Proceedings Radar Sonar and Navigation, 146 (1). pp. 2-7.

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Abstract

The Kalman filter provides an effective solution to the linear Gaussian filtering problem. However where there is nonlinearity, either in the model specification or the observation process, other methods are required. Methods known generically as `particle filters' are considered. These include the condensation algorithm and the Bayesian bootstrap or sampling importance resampling (SIR) filter. These filters represent the posterior distribution of the state variables by a system of particles which evolves and adapts recursively as new information becomes available. In practice, large numbers of particles may be required to provide adequate approximations and for certain applications, after a sequence of updates, the particle system will often collapse to a single point. A method of monitoring the efficiency of these filters is introduced which provides a simple quantitative assessment of sample impoverishment and the authors show how to construct improved particle filters that are both structurally efficient in terms of preventing the collapse of the particle system and computationally efficient in their implementation. This is illustrated with the classic bearings-only tracking problem.

Item Type:
Journal Article
Journal or Publication Title:
IEE Proceedings Radar Sonar and Navigation
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/1700/1705
Subjects:
ID Code:
49092
Deposited By:
Deposited On:
21 Jul 2011 15:18
Refereed?:
Yes
Published?:
Published
Last Modified:
24 Nov 2020 01:45