Adaptive Kalman Filter for Noise Identification
نویسنده
چکیده
This paper deals with an on line identification of the values of these entities by a suitable adjusting of the filter gain and incorporating information about the quality of innovation sequence. The result yields a new adaptive Kalman filter. The proposal is mainly based on statistical properties of the innovation sequence of the filter and improves some previous results pointed out by Bellanger and Mehra. Particularly, the autocorrelation function of the innovation sequence is used. Furthermore, information about the quality of the autocorrelation parameter is incorporated through a weighted least squares methodology. The determination of the weights is based on a distance criterion, which involves the ideal probability distribution and the current probability referring to the first and second order statistics of autocorrelation functions. The estimation of the a priori noise statistics Q and R is obtained straightforwardly from the preceding and the optimal gain and the innovation covariance of the filter.
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