Square root normalized feedback ladder algorithm for the identification of moving average systems

Abstract

We have presented a square root normalized version of the feedback ladder algorithm for the identification of the parameters of a moving average model. The number of equations needed is reduced from eight in the unnormalized case to just five. The complexity of the equations increases but the procedure is justified because it seems to lead to a more convenient hardware realization. Moreover, this realization would be completely similar (for the backward and forward residuals lines) to the CORDIC processors implementation already proposed for the feedlorward ladder algorithms (FFLA). A possible disadvantage is that three of the variables used may have magnitudes greater than one. However the essential feature of the FBLA, that of being able to read out directly the estimated coefficients of the -monic-polynomial model is not modified.