Cascade recursive least squares with subsection adaptation for AR parameter estimation

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 here 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. 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.