Open Access

A Concept of Approximated Densities for Efficient Nonlinear Estimation

EURASIP Journal on Advances in Signal Processing20022002:146917

Received: 31 July 2001

Published: 22 October 2002


This paper presents the theoretical development of a nonlinear adaptive filter based on a concept of filtering by approximated densities (FAD). The most common procedures for nonlinear estimation apply the extended Kalman filter. As opposed to conventional techniques, the proposed recursive algorithm does not require any linearisation. The prediction uses a maximum entropy principle subject to constraints. Thus, the densities created are of an exponential type and depend on a finite number of parameters. The filtering yields recursive equations involving these parameters. The update applies the Bayes theorem. Through simulation on a generic exponential model, the proposed nonlinear filter is implemented and the results prove to be superior to that of the extended Kalman filter and a class of nonlinear filters based on partitioning algorithms.


nonlinear estimationnonlinear adaptive filterexponential distributionmaximum entropynon-Gaussian signal processing

Authors’ Affiliations

Department of Cybernetics, The University of Reading, Whiteknights, UK


© Ruiz 2002