Non Informative Bayesian Dispersion Particle Filter

Résumé: In this research paper, we attempt to introduce a new algorithm for filtering a state space model. The observations of this algorithm follow an exponential dispersion model. The paper focuses here on the inclusion of non informative prior knowledge in parameter estimation on non linear state space models using an improper uniform prior measure. Therefore, a new particle filter is introduced. Conventional Particle Filter (PF) produces an incorrect sample from a discrete approximation distribution. This new algorithm is regularized continuous distribution method which is obtained with the exponential dispersion model. A necessary and sufficient condition for existence and the convergence of the non informative Bayesian estimator of dispersion parameter, are established. This methodology extends the classical PF implemented by this new estimation method for exponential dispersion models framework using non informative Bayesian approach. In order to evaluate the performance of the proposed algorithm, a case study with the simulations and microscopic image restoration are carried out. The results exhibit a great performance improvement from the proposed approach.

Publié dans la revue: Journal of innovative applied mathematics and computational sciences