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- W2099933529 abstract "We propose the adaptive cascade recursive least squares (CRLS-SA) algorithm for the estimation of linear prediction, or AR model, coefficients. The CRLS-SA algorithm features low computational complexity since each section is adapted independently from the other sections. It is shown that the CRLS-SA algorithm can yield AR coefficient estimates closer to the true values, for some known signals, than the widely used autocorrelation method. The CRLS-SA converges faster to the true values of the model, which is critically important for estimation from short data records. While the computational effort of CRLS-SA is a factor of 3 to 4 higher than that for the autocorrelation method, the improvement in performance yields a viable alternative for a number of applications." @default.
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- W2099933529 date "2002-11-27" @default.
- W2099933529 modified "2023-09-26" @default.
- W2099933529 title "Cascade recursive least squares with subsection adaptation for AR parameter estimation" @default.
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- W2099933529 doi "https://doi.org/10.1109/icassp.1998.675424" @default.
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