A maximum entropy Kalman filter for signal reconstruction

Author

David, A. ; Aboulnasr, T.

Author_Institution

Sch. of Inf. Technol. & Eng., Ottawa Univ., Ont., Canada

Volume

4

fYear

1999

fDate

36342

Firstpage

151

Abstract

In this paper, we propose a Maximum Entropy Kalman Filter (MEKF) and its application in image recovery. The proposed 2-D MEKF employs Maximum Entropy (ME) as its optimization criterion to identify the appropriate parameters of a standard Kalman filter. The strength of the ME based filters is due to the fact that these filters make no assumptions regarding the unobserved data, and avoid the over-smoothing that is associated with the Mean Square Error (MSE) based algorithms. Furthermore, we address the issues of ME 2-D separable filter expansion and the finite constraint bound on the reconstructed pixels

Keywords

Kalman filters; filtering theory; image reconstruction; matrix algebra; maximum entropy methods; optimisation; 2D separable filter expansion; finite constraint bound; image recovery; maximum entropy Kalman filter; optimization criterion; reconstructed pixels; signal reconstruction; Autocorrelation; Cost function; Entropy; Filtering; Filters; Laboratories; Optimization methods; Signal processing; Signal reconstruction; Stochastic processes;

fLanguage

English

Publisher

ieee

Conference_Titel

Circuits and Systems, 1999. ISCAS '99. Proceedings of the 1999 IEEE International Symposium on

Conference_Location

Orlando, FL

Print_ISBN

0-7803-5471-0

Type

conf

DOI

10.1109/ISCAS.1999.779964

Filename

779964