Title :
Linear filtering with timing uncertainty
Author_Institution :
Dept. of EEE, Univ. of Melbourne, Melbourne, VIC
fDate :
June 30 2008-July 3 2008
Abstract :
Linear filtering in the presence of timing uncertainty is considered. In the model assumed here the true measurement times are intermittently available and noisy measurement times are always available. The estimation problem involves jointly estimating the state and the timing error parameters. The optimal Bayesian estimator cannot be found in closed-form so three approximations are proposed. The first estimates parameters only when the true measurement time is available, the second replaces unknown measurement times by estimates and the third is based on a sequential Monte Carlo approximation to the posterior distribution of the measurement time sequence. A performance analysis via numerical simulations shows that the best performance is achieved by the sequential Monte Carlo method, even with reasonably small sample sizes and a mismatched timing error distribution.
Keywords :
Monte Carlo methods; filtering theory; parameter estimation; state estimation; linear filtering; mismatched timing error distribution; optimal Bayesian estimator; sequential Monte Carlo approximation; state estimation; timing error parameters; timing uncertainty; Filtering; sequential Monte carlo methods; timing errors;
Conference_Titel :
Information Fusion, 2008 11th International Conference on
Conference_Location :
Cologne
Print_ISBN :
978-3-8007-3092-6
Electronic_ISBN :
978-3-00-024883-2