Nonlinear state space smoothing using the conditional particle filter
Computation
2015-09-17 v3 Systems and Control
Optimization and Control
Abstract
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 ultra-wideband 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.
Cite
@article{arxiv.1502.03697,
title = {Nonlinear state space smoothing using the conditional particle filter},
author = {Andreas Svensson and Thomas B. Schön and Manon Kok},
journal= {arXiv preprint arXiv:1502.03697},
year = {2015}
}
Comments
Accepted for the 17th IFAC Symposium on System Identification (SYSID), Beijing, China, October 2015