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Maximum Likelihood State Estimation of Semi-Markovian Switching System in Non-Gaussian Measurement Noise.
- Source :
-
IEEE Transactions on Aerospace & Electronic Systems . Jan2010, Vol. 46 Issue 1, p133-146. 14p. - Publication Year :
- 2010
-
Abstract
- In the work presented here, we consider state and parameter estimation of a semi-nonlinear Markov jump system in a non-Gaussian noise environment. The non-Gaussian measurement noise is approximated by a finite Gaussian mixture model (GMM). We propose a maximum likelihood (ML) solution to this state estimation problem which leads to two expectation-maximization (EM) algorithms. The first is a batch EM method which takes all the available data in the conditional expectation of the state in the E-step. An interacting multiple model (IMM) smoother is employed to evaluate the conditional expectation of the state by which a suboptimal estimate of system state is directly obtained. The Gaussian mixture parameters are then updated in the M-step. The second is a recursive EM algorithm which results from a stochastic approximation procedure and uses a standard IMM filter. For performance evaluation, posterior Cramer-Rao bound (PCRB) on the state estimation is adopted. Simulation results verify the effectiveness of the oposed algorithms. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 00189251
- Volume :
- 46
- Issue :
- 1
- Database :
- Academic Search Index
- Journal :
- IEEE Transactions on Aerospace & Electronic Systems
- Publication Type :
- Academic Journal
- Accession number :
- 48546303
- Full Text :
- https://doi.org/10.1109/TAES.2010.5417152