ON THE OPTIMUM DESIGN OF THE BLOCK ADAPTIVE FIR DIGITAL-FILTER

This paper presents a general optimum block adaptive (GOBA) algorithm for adaptive FIR filtering. In this algorithm, the correction terms of the filter coefficients in each block, instead of the convergence factors, are optimized in a least squares sense. Unlike the optimum block adaptive algorithm with individual adaptation of parameters (OBAI) proposed by Mikhael and Wu, which can be used only for the case where the block length L is greater than or equal to the filter-tap number N, the GOBA algorithm has no constraints on L and N. It is shown that the GOBA algorithm is reduced to the normalized LMS algorithm when L = 1 and is equivalent to the OBAI algorithm when L greater-than-or-equal-to N. The convergence of the GOBA algorithm can be assured if the correlation matrix of the input signal is positive definite. Computer simulations based on an efficient computing procedure support that the GOBA algorithm achieves faster convergence with slightly degraded convergence accuracy in stationary environments and better weight tracking capability in nonstationary environments as compared to existing block adaptive algorithms with no constraints on L and N.