Nonlinear State Space Smoothing Using the Conditional Particle Filter
2015 (English)In: Proceedings of the 17th IFAC Symposium on System Identification, 2015, 975-980 p.Conference paper (Refereed)
To estimate the smoothing distribution in a nonlinear state space model, we apply the conditional particle filter with ancestor sampling. This gives an iterative algorithm in a Markov chain Monte Carlo fashion, with asymptotic convergence results. The computational complexity is analyzed, and our proposed algorithm is successfully applied to the challenging problem of sensor fusion between ultrawideband and accelerometer/gyroscope measurements for indoor positioning. It appears to be a competitive alternative to existing nonlinear smoothing algorithms, in particular the forward filtering-backward simulation smoother.
Place, publisher, year, edition, pages
2015. 975-980 p.
Smoothing, Particle filters, Nonlinear systems, State estimation, Monte Carlo method, Sensor fusion, Position estimation.
IdentifiersURN: urn:nbn:se:liu:diva-123955OAI: oai:DiVA.org:liu-123955DiVA: diva2:894484
17th IFAC Symposium on System Identification, Beijing, China, October 19-21, 2015
ProjectsThe project Probabilistic modeling of dynamical systems (Contract number: 621- 2013-5524)CADICS
FunderSwedish Research Council