Two-dimensional block adaptive filtering algorithms

A technique for 2-D system identification which processes 2-D signals using 2-D blocks is proposed. Two algorithms which perform 2-D FIR (finite impulse response) adaptive filtering using 2-D error blocks or windows are presented. The first algorithm uses a convergence factor that is constant for each 2-D coefficient at each window iteration. This algorithm is termed the two-dimensional block least mean square algorithm (TDBLMS). A novel 2-D adaptive fast LMS algorithm which processes 2-D signals is presented. In this algorithm, a convergence factor is obtained that is the same for all 2-D coefficients at a particular window iteration, but is updated at each window iteration. This algorithm is called the two-dimensional optimum block algorithm (TDOBA). The convergence properties of the TDBLMS and TDOBA are investigated and compared using computer simulations for both disjoint and overlapping windows. It is shown that the TDOBA clearly outperforms the TDBLMS algorithm with respect to convergence speed and accuracy of adaptation