Volume 16, Issue 2
An Implicit Algorithm of Solving Nonlinear Filtering Problems

Feng Bao, Yanzhao Cao & Xiaoying Han

Commun. Comput. Phys., 16 (2014), pp. 382-402.

Published online: 2014-08

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  • Abstract

Nonlinear filter problems arise in many applications such as communications and signal processing. Commonly used numerical simulation methods include Kalman filter method, particle filter method, etc. In this paper a novel numerical algorithm is constructed based on samples of the current state obtained by solving the state equation implicitly. Numerical experiments demonstrate that our algorithm is more accurate than the Kalman filter and more stable than the particle filter.

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@Article{CiCP-16-382, author = {}, title = {An Implicit Algorithm of Solving Nonlinear Filtering Problems}, journal = {Communications in Computational Physics}, year = {2014}, volume = {16}, number = {2}, pages = {382--402}, abstract = {

Nonlinear filter problems arise in many applications such as communications and signal processing. Commonly used numerical simulation methods include Kalman filter method, particle filter method, etc. In this paper a novel numerical algorithm is constructed based on samples of the current state obtained by solving the state equation implicitly. Numerical experiments demonstrate that our algorithm is more accurate than the Kalman filter and more stable than the particle filter.

}, issn = {1991-7120}, doi = {https://doi.org/10.4208/cicp.180313.130214a}, url = {http://global-sci.org/intro/article_detail/cicp/7047.html} }
TY - JOUR T1 - An Implicit Algorithm of Solving Nonlinear Filtering Problems JO - Communications in Computational Physics VL - 2 SP - 382 EP - 402 PY - 2014 DA - 2014/08 SN - 16 DO - http://doi.org/10.4208/cicp.180313.130214a UR - https://global-sci.org/intro/article_detail/cicp/7047.html KW - AB -

Nonlinear filter problems arise in many applications such as communications and signal processing. Commonly used numerical simulation methods include Kalman filter method, particle filter method, etc. In this paper a novel numerical algorithm is constructed based on samples of the current state obtained by solving the state equation implicitly. Numerical experiments demonstrate that our algorithm is more accurate than the Kalman filter and more stable than the particle filter.

Feng Bao, Yanzhao Cao & Xiaoying Han. (2020). An Implicit Algorithm of Solving Nonlinear Filtering Problems. Communications in Computational Physics. 16 (2). 382-402. doi:10.4208/cicp.180313.130214a
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