Efficient adaptive transversal algorithms for least squares ARMA identification

Two efficient algorithmic families are developed for multichannel combiners characterized by unequal memory lengths for each of two input channels. The difference between the proposed methods lies in the way the Kalman gain vector is order-updated in each case. The first algorithm operates on both inputs simultaneously, utilizing block multichannel structured order recursions. The resulting scheme is called the diagonal update algorithm. The second approach updates the Kalman gain in a two-step procedure by reducing first the size of one input and then the size of the other input. The resulting method is called the stairwise update algorithm. Both algorithms are applicable to adaptive ARMA (autoregressive moving average) system identification, adaptive control, and the design of decision-feedback equalizers. Simulation results are included