An Improved Method of Kalman Filter Based on Neural Network

Jiang Ens-ong; Li Meng-chao; Sun Liu-jie

doi:10.3724/SP.J.1146.2006.00188

Volume 29 Issue 9

Jan. 2011

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Article Navigation > Journal of Electronics & Information Technology > 2007 > 29(9): 2073-2076

Jiang Ens-ong, Li Meng-chao, Sun Liu-jie. An Improved Method of Kalman Filter Based on Neural Network[J]. Journal of Electronics & Information Technology, 2007, 29(9): 2073-2076. doi: 10.3724/SP.J.1146.2006.00188

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Jiang Ens-ong, Li Meng-chao, Sun Liu-jie. An Improved Method of Kalman Filter Based on Neural Network[J]. Journal of Electronics & Information Technology, 2007, 29(9): 2073-2076. doi: 10.3724/SP.J.1146.2006.00188

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An Improved Method of Kalman Filter Based on Neural Network

doi: 10.3724/SP.J.1146.2006.00188 cstr: 32379.14.SP.J.1146.2006.00188

Received Date: 2006-02-23
Rev Recd Date: 2006-08-14
Publish Date: 2007-09-19

Abstract

Abstract

Kalman filter is a recursive filtering method based on minimum variance estimation, but it assumes that the signals state model is exactly known, which restricts its application in practice. Nevertheless the systems state equation can be obtained through the identification of systems by using neural networks good abilities of non-linear mapping. In contrast to some classic improved algorithm of Kalman filtering, this method has the advantages of wide application range, simple and feasible mathematical modeling. In this paper, the method which integrates neural network and Kalman filter is implemented for image restoration. The experiment result shows that the provided method is effective and available.

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References

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