Title :
Particle based MAP state estimation: A comparison
Author :
Saha, S. ; Boers, Y. ; Driessen, H. ; Mandal, P.K. ; Bagchi, A.
Author_Institution :
Dept. of Appl. Math., Univ. of Twente, Enschede, Netherlands
Abstract :
MAP estimation is a good alternative to MMSE for certain applications involving nonlinear non Gaussian systems. Recently a new particle filter based MAP estimator has been derived. This new method extracts the MAP directly from the output of a running particle filter. In the recent past, a Viterbi algorithm based MAP sequence estimator has been developed. In this paper, we compare these two methods for estimating the current state and the numerical results show that the former performs better.
Keywords :
Monte Carlo methods; least mean squares methods; maximum likelihood estimation; nonlinear filters; particle filtering (numerical methods); MAP sequence; MMSE; Monte Carlo method; Viterbi algorithm; maximum a posteriori estimate; minimum mean square error method; nonlinear non Gaussian system; particle filter based MAP state estimation; Aircraft navigation; Airplanes; Bayesian methods; Clouds; Density measurement; Mathematics; Monte Carlo methods; Particle filters; State estimation; Viterbi algorithm; Bayesian point estimation; filter MAP; particle filter; sequential Monte Carlo;
Conference_Titel :
Information Fusion, 2009. FUSION '09. 12th International Conference on
Conference_Location :
Seattle, WA
Print_ISBN :
978-0-9824-4380-4
