Sequential estimation of random parameters under model uncertainty

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