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Sequential estimation of random parameters under model uncertainty

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1 Author(s)
Djuric, P.M. ; Dept. of Electr. & Comput. Eng., State Univ. of New York, Stony Brook, NY

In many signal processing problems, the estimation of random parameters must be carried out sequentially and under model uncertainty. In the paper, a Bayesian approach is proposed for solving this problem, which is based on sequential updating of the posterior distribution of the desired parameters. It is shown that under a certain general set of conditions, the posterior of the unknown parameters is a mixture density. Since the computation of the solution becomes very intensive as the number of data (records) grows, a numerical procedure is proposed based on the sequential importance sampling scheme. Its number of computations per new data record is constant, and the procedure can easily be implemented in parallel

Published in:

Acoustics, Speech, and Signal Processing, 2000. ICASSP '00. Proceedings. 2000 IEEE International Conference on  (Volume:1 )

Date of Conference:

2000

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