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“Mean-Square Filtering for Polynomial System States Confused with Poisson Noises over Polynomial Observations”

Authors: Michael Basin, Juan J. Maldonado and Hamid Reza Karimi,
Affiliation: University of Nuevo Leon and University of Agder
Reference: 2011, Vol 32, No 2, pp. 47-55.

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Keywords: Filter Design; Poisson Noises; Polynomial Observations

Abstract: In this paper, the mean-square filtering problem for polynomial system states confused with white Poisson noises over polynomial observations is studied proceeding from the general expression for the stochastic Ito differentials of the mean-square estimate and the error variance. In contrast to the previously obtained results, the paper deals with the general case of nonlinear polynomial states and observations with white Poisson noises. As a result, the Ito differentials for the mean-square estimate and error variance corresponding to the stated filtering problem are first derived. The procedure for obtaining an approximate closed-form finite-dimensional system of the filtering equations for any polynomial state over observations with any polynomial drift is then established. In the example, the obtained closed-form filter is applied to solve the third order sensor filtering problem for a quadratic state, assuming a conditionally Poisson initial condition for the extended third order state vector. The simulation results show that the designed filter yields a reliable and rapidly converging estimate.

PDF PDF (334 Kb)        DOI: 10.4173/mic.2011.2.1

DOI forward links to this article:
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  title={{Mean-Square Filtering for Polynomial System States Confused with Poisson Noises over Polynomial Observations}},
  author={Basin, Michael and Maldonado, Juan J. and Karimi, Hamid Reza},
  journal={Modeling, Identification and Control},
  publisher={Norwegian Society of Automatic Control}


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