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Analysis of nonlinear state space model with dependent measurement noises
Authors:A. Hajrajabi
Affiliation:Department of Statistics, Faculty of Basic Sciences, Imam Khomeini International University, Qazvin, Iran
Abstract:This paper presents a nonlinear state space model with considering a first-order autoregressive model for measurement noises. A recursive method using Taylor series based approximations for filtering, prediction and smoothing problem of hidden states from the noisy observations is designed. Also, an expectation-maximization algorithm for calculating the maximum likelihood estimators of parameters is presented. The closed form solutions are obtained for estimating of the hidden states and the unknown parameters. Finally, the performance of the designed methods are verified in a simulation study.
Keywords:EM algorithm  Estimation of the hidden state  Nonlinear state space model  Maximum likelihood estimation.
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