Approximate conditional mean particle filter
Dept. of Electr. & Comput. Eng., McMaster Univ., Hamilton, Ont.
DOI: 10.1109/SSP.2005.1628629 Conference: Statistical Signal Processing, 2005 IEEE/SP 13th Workshop on
We consider partially observed non-Gaussian dynamic state space models in which the process equation consists of a combination of linear and nonlinear states and the process noise for the nonlinear state update is distributed according to a mixture of Gaussians. In this paper, we solve a Bayesian filtering problem. The proposed filter is an efficient combination of the particle filter and the approximate conditional mean filter. Simulation results on a time-varying autoregressive signal demonstrate the effectiveness of the proposed algorithm
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