A fuzzy-tuned adaptive Kalman filter
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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.
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filtering and prediction theory
fuzzy set theory