DocumentCode :
3419378
Title :
Bayesian estimation of transition probabilities in hybrid systems via convex optimization
Author :
Wang, Gang ; Yang, Kehu
Author_Institution :
ISN Lab., Xidian Univ., Xi´´an
fYear :
2008
fDate :
March 31 2008-April 4 2008
Firstpage :
3453
Lastpage :
3456
Abstract :
In practice, the transition probability matrix (TPM) in the approach to track a maneuvering target is often unknown. We propose a new method to estimate the optimal TPM according to the maximum a posteriori (MAP) or maximum likelihood (ML) criterion via convex optimization. We apply the proposed method to the nonlinear/non- Gaussian cases, where the interacting multiple model (IMM) particle filter (IMMPF) is employed to estimate the corresponding base state. Simulation results of tracking a maneuvering target show the efficacy of the proposed method with improved performance.
Keywords :
Bayes methods; maximum likelihood estimation; optimisation; Bayesian estimation; convex optimization; hybrid systems; interacting multiple model particle filter; maximum a posteriori; maximum likelihood criterion; transition probabilities; transition probability matrix; Adaptive estimation; Bayesian methods; Maximum likelihood estimation; Mean square error methods; Optimization methods; Performance loss; Recursive estimation; State estimation; State-space methods; Target tracking; Adaptive estimation; Convex optimization; Hybrid systems; IMMPF; MAP/ML Estimation;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Acoustics, Speech and Signal Processing, 2008. ICASSP 2008. IEEE International Conference on
Conference_Location :
Las Vegas, NV
ISSN :
1520-6149
Print_ISBN :
978-1-4244-1483-3
Electronic_ISBN :
1520-6149
Type :
conf
DOI :
10.1109/ICASSP.2008.4518394
Filename :
4518394
Link To Document :
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