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

Research output: Chapter in Book/Report/Conference proceedingPaper in conference proceeding

Abstract

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.

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Subject classification (UKÄ) – MANDATORY

  • Control Engineering
Original languageEnglish
Title of host publication[Host publication title missing]
PublisherIEEE--Institute of Electrical and Electronics Engineers Inc.
Pages805-812
Publication statusPublished - 2013
Publication categoryResearch
Peer-reviewedYes
Event16th International Conference on Information Fusion, 2013 - Istanbul, Turkey
Duration: 2013 Jul 92013 Jul 12
Conference number: 16

Conference

Conference16th International Conference on Information Fusion, 2013
CountryTurkey
CityIstanbul
Period2013/07/092013/07/12

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