A robust particle filter for state estimation - with convergence results
Particle filters are becoming increasingly important and useful for state estimation in nonlinear systems. Many filter versions have been suggested, and several results on convergence of filter properties have been reported. However, apparently a result on the convergence of the state estimate itsel...
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| creator | Xiao-Li Hu Schon, T.B. Ljung, L. |
| description | Particle filters are becoming increasingly important and useful for state estimation in nonlinear systems. Many filter versions have been suggested, and several results on convergence of filter properties have been reported. However, apparently a result on the convergence of the state estimate itself has been lacking. This contribution describes a general framework for particle filters for state estimation, as well as a robustified filter version. For this version a quite general convergence result is established. In particular, it is proved that the particle filter estimate convergences w.p.1 to the optimal estimate, as the number of particles tends to infinity. |
| doi_str_mv | 10.1109/CDC.2007.4434208 |
| format | Conference Proceeding |
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Many filter versions have been suggested, and several results on convergence of filter properties have been reported. However, apparently a result on the convergence of the state estimate itself has been lacking. This contribution describes a general framework for particle filters for state estimation, as well as a robustified filter version. For this version a quite general convergence result is established. In particular, it is proved that the particle filter estimate convergences w.p.1 to the optimal estimate, as the number of particles tends to infinity.</abstract><pub>IEEE</pub><doi>10.1109/CDC.2007.4434208</doi><tpages>6</tpages></addata></record> |
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| ispartof | 2007 46th IEEE Conference on Decision and Control, 2007, p.312-317 |
| issn | 0191-2216 |
| language | eng |
| recordid | cdi_ieee_primary_4434208 |
| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Convergence Equations Filtering Noise measurement Nonlinear dynamical systems Nonlinear estimation Nonlinear systems Particle filtering Particle filters Robustness State estimation Stochastic processes TECHNOLOGY TEKNIKVETENSKAP Time measurement |
| title | A robust particle filter for state estimation - with convergence results |
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