A constrained two-dimensional adaptive digital filter with reduced computational complexity

An adaptive 2-D digital filter is presented in which the filter is determined by a 1-D FIR (finite-impulse-response) prototype filter, and hence the computational complexity of the coefficient-update algorithm has an order of complexity that is similar to a 1-D adaptive filter. The convergence rate of the proposed structure is much faster than that of a direct-form 2-D LMS (least-mean-square) filter. Results of the hardware realization of the filter are presented that show its learning curve to be considerably less noisy than that of the 2-D direct-form LMS filter.<>