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A fuzzy-tuned adaptive Kalman filter

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Title: A fuzzy-tuned adaptive Kalman filter
Author: Painter, John H.; Young Hwan Lho
Abstract: In this paper, fuzzy processing is applied to the adaptive Kalman filter. The filter gain coefficients are adapted over a 50 dB range of unknown signal/noise dynamics, using fuzzy membership functions. Specific simulation results are shown for a dynamic system model which has position-velocity states, as in vehicle tracking applications such as the global positioning system (GPS). The filter is single-input single-output, driven by measurements of position, corrupted by additive (Gaussian) noise. The fuzzy adaptation technique is also applicable to multiple-input multiple-output applications for the cases where the states are higher-order moments of motion. The fuzzy processing is driven by an inaccurate online estimate of signal-to-noise ratio for the signal being tracked. A robust Bayes scheme calculates the filter gain coefficients from the signal-to-noise estimate. In our implementation, the inaccurate signal-to-noise estimate is corrected by the use of fuzzy membership functions. Performance comparisons are given between optimum, fuzzy-tuned adaptive, and fixed-gain Kalman filters for the second-order position-velocity model.
Description: ©1993 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Publisher: IEEE
Subject: Bayes methods
Kalman filters
adaptive filters
filtering and prediction theory
fuzzy set theory
Department: Aerospace Engineering
URI: 10.1109/IFIS.1993.324197
Date: 1993-12-01


Painter, J.H., Young Hwan Lho (1993). A fuzzy-tuned adaptive Kalman filter. Third International Conference on Industrial Fuzzy Control and Intelligent Systems, 1993, IFIS '93: 144-148.

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