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Volume 36, Issue 4
A Splitting Method for Nonlinear Filtering Problems with Diffusive and Point Process Observations

Fengshan Zhang, Yongkui Zou, Shimin Chai & Yanzhao Cao

Commun. Comput. Phys., 36 (2024), pp. 996-1020.

Published online: 2024-10

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  • Abstract

This paper aims to develop and analyze a comprehensive discretized splitting-up numerical scheme for the Zakai equation. This equation arises from a nonlinear filtering problem, where observations incorporate noise modeled by point processes and Wiener processes. Initially, we introduce a regularization parameter and employ a splitting-up approach to break down the Zakai equation into two stochastic differential equations and a partial differential equation (PDE). Subsequently, we employ a finite difference scheme for the temporal dimension and the spectral Galerkin method for the spatial dimension to achieve full discretization of these equations. This results in a numerical solution for the Zakai equation using the splitting-up technique. We demonstrate that this numerical solution converges to the exact solution with a convergence order of $\frac{1}{2}.$ Additionally, we conduct several numerical experiments to illustrate and validate our theoretical findings.

  • AMS Subject Headings

35K25, 35K55, 35K91, 65M12, 65M15

  • Copyright

COPYRIGHT: © Global Science Press

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@Article{CiCP-36-996, author = {Zhang , FengshanZou , YongkuiChai , Shimin and Cao , Yanzhao}, title = {A Splitting Method for Nonlinear Filtering Problems with Diffusive and Point Process Observations}, journal = {Communications in Computational Physics}, year = {2024}, volume = {36}, number = {4}, pages = {996--1020}, abstract = {

This paper aims to develop and analyze a comprehensive discretized splitting-up numerical scheme for the Zakai equation. This equation arises from a nonlinear filtering problem, where observations incorporate noise modeled by point processes and Wiener processes. Initially, we introduce a regularization parameter and employ a splitting-up approach to break down the Zakai equation into two stochastic differential equations and a partial differential equation (PDE). Subsequently, we employ a finite difference scheme for the temporal dimension and the spectral Galerkin method for the spatial dimension to achieve full discretization of these equations. This results in a numerical solution for the Zakai equation using the splitting-up technique. We demonstrate that this numerical solution converges to the exact solution with a convergence order of $\frac{1}{2}.$ Additionally, we conduct several numerical experiments to illustrate and validate our theoretical findings.

}, issn = {1991-7120}, doi = {https://doi.org/10.4208/cicp.OA-2024-0075}, url = {http://global-sci.org/intro/article_detail/cicp/23484.html} }
TY - JOUR T1 - A Splitting Method for Nonlinear Filtering Problems with Diffusive and Point Process Observations AU - Zhang , Fengshan AU - Zou , Yongkui AU - Chai , Shimin AU - Cao , Yanzhao JO - Communications in Computational Physics VL - 4 SP - 996 EP - 1020 PY - 2024 DA - 2024/10 SN - 36 DO - http://doi.org/10.4208/cicp.OA-2024-0075 UR - https://global-sci.org/intro/article_detail/cicp/23484.html KW - Nonlinear filtering problem, Zakai equation, splitting-up technique, error analysis. AB -

This paper aims to develop and analyze a comprehensive discretized splitting-up numerical scheme for the Zakai equation. This equation arises from a nonlinear filtering problem, where observations incorporate noise modeled by point processes and Wiener processes. Initially, we introduce a regularization parameter and employ a splitting-up approach to break down the Zakai equation into two stochastic differential equations and a partial differential equation (PDE). Subsequently, we employ a finite difference scheme for the temporal dimension and the spectral Galerkin method for the spatial dimension to achieve full discretization of these equations. This results in a numerical solution for the Zakai equation using the splitting-up technique. We demonstrate that this numerical solution converges to the exact solution with a convergence order of $\frac{1}{2}.$ Additionally, we conduct several numerical experiments to illustrate and validate our theoretical findings.

Zhang , FengshanZou , YongkuiChai , Shimin and Cao , Yanzhao. (2024). A Splitting Method for Nonlinear Filtering Problems with Diffusive and Point Process Observations. Communications in Computational Physics. 36 (4). 996-1020. doi:10.4208/cicp.OA-2024-0075
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