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.
Original language | English |
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Title of host publication | [Host publication title missing] |
Publisher | IEEE - Institute of Electrical and Electronics Engineers Inc. |
Pages | 805-812 |
Publication status | Published - 2013 |
Event | 16th International Conference on Information Fusion, 2013 - Istanbul, Turkey Duration: 2013 Jul 9 → 2013 Jul 12 Conference number: 16 |
Conference
Conference | 16th International Conference on Information Fusion, 2013 |
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Country/Territory | Turkey |
City | Istanbul |
Period | 2013/07/09 → 2013/07/12 |
Subject classification (UKÄ)
- Control Engineering