A concept of approximated densities for efficient nonlinear estimation
Ruiz, V. F. (2002) A concept of approximated densities for efficient nonlinear estimation. EURASIP Journal on Advances in Signal Processing, 2002 (10). pp. 1145-1150. ISSN 1687-6172
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To link to this article DOI: 10.1155/S1110865702207027
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.