Complete Real Time Solution of the General Nonlinear Filtering Problem Without Memory
Xue Jiao Luo, Stephen S.‐T. Yau · IEEE Transactions on Automatic Control · 2013
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 L1sense. And the precise error has been estimated. Finally, the numerical simulation support the feasibility and efficiency of our algorithm.