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Rao-Blackwellized Out-of-Sequence Processing for Mixed Linear/Nonlinear State-Space Models

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Author

Summary, in English

We investigate the out-of-sequence measurements particle filtering problem for a set of conditionally linear Gaussian state-space models, known as mixed linear/nonlinear state-space models. Two different algorithms are proposed, which both exploit the conditionally linear substructure. The first approach is based on storing only a subset of the particles and their weights, which implies low memory and computation requirements. The second approach is based on a recently reported Rao-Blackwellized forward filter/backward simulator, adapted to the out-of-sequence filtering task with computational considerations for enabling online implementations. Simulation studies on two examples show that both approaches outperform recently reported particle filters, with the second approach being superior in terms of tracking performance.

Publishing year

2013

Language

English

Pages

805-812

Publication/Series

[Host publication title missing]

Document type

Conference paper

Publisher

IEEE - Institute of Electrical and Electronics Engineers Inc.

Topic

  • Control Engineering

Conference name

16th International Conference on Information Fusion, 2013

Conference date

2013-07-09 - 2013-07-12

Conference place

Istanbul, Turkey

Status

Published

Project

  • ENGROSS

Research group

  • LCCC