Title
Complete Real Time Solution of the General Nonlinear Filtering Problem Without Memory
Abstract
It is well known that the nonlinear filtering problem has important applications in both military and civil industries. The central problem of nonlinear filtering is to solve the Duncan-Mortensen-Zakai (DMZ) equation in real time and in a memoryless manner. In this paper, we shall extend the algorithm developed previously by S.-T. Yau and the second author to the most general setting of nonlinear filterings, where the explicit time-dependence is in the drift term, observation term, and the variance of the noises could be a matrix of functions of both time and the states. To preserve the off-line virtue of the algorithm, necessary modifications are illustrated clearly. Moreover, it is shown rigorously that the approximated solution obtained by the algorithm converges to the real solution in the L1 sense. And the precise error has been estimated. Finally, the numerical simulation support the feasibility and efficiency of our algorithm.
Year
DOI
Venue
2013
10.1109/TAC.2013.2264552
IEEE Trans. Automat. Contr.
Keywords
Field
DocType
Equations,Mathematical model,Real-time systems,Approximation algorithms,Algorithm design and analysis,Approximation methods,Noise
Approximation algorithm,Mathematical optimization,Algorithm design,Nonlinear system,Computer simulation,Matrix (mathematics),Control theory,Nonlinear filtering,Mathematics
Journal
Volume
Issue
ISSN
58
10
0018-9286
Citations 
PageRank 
References 
12
0.98
3
Authors
2
Name
Order
Citations
PageRank
Xue Luo1242.63
Stephen S Yau21768193.24