A New Filtering Algorithm for Stochastic Dynamical Systems with State-Dependent Observation Noise

TANG Jian-Fang; LI Jian-Bo; ZHOU Jie

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A New Filtering Algorithm for Stochastic Dynamical Systems with State-Dependent Observation Noise

TANG Jian-Fang 1, LI Jian-Bo 2, ZHOU Jie 3

1. Department of Science, University of Noname, City 112233

2. College of Mathematics, Sichuan University, Chengdu 610064

*Correspondence author

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Funding: Specialized Research Fund for the Doctoral Program of Higher Education (20130181110042)%***Foundation （No.00000000）

Opened online:20 December 2016

Accepted by: none

Citation: TANG Jian-Fang, LI Jian-Bo, ZHOU Jie.A New Filtering Algorithm for Stochastic Dynamical Systems with State-Dependent Observation Noise[OL]. [20 December 2016] http://en.paper.edu.cn/en_releasepaper/content/4712079

Much research on extensions of Kalman filtering for models with state-dependent noise has been done in the past decades. This paper discusses a dynamic system with observation noise dependent on estimated state, where the correlation between them is described by a nonzero covariance matrix. We propose a new filtering algorithm, which shows a better performance than the standard Kalman filtering in this case. Numerical example is provided to verify its performance.

Keywords:filter algorithm, Kalman filtering, state-dependent observation noise, new filtering algorithm.

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