Filter architectures and adaptive algorithms for 2-D adaptive digital signal processing

A 2-D adaptive filter structure based on the McClellan transformation design technique for 2-D FIR (finite impulse response) filters is proposed as a means of achieving improved performance. Performance is compared with that of a direct-form 2-D LMS (least mean squares) structure in terms of learning characteristics and computational efficiency. It is shown that if the transformation structure is constrained by a priori knowledge of contour shapes in the frequency domain, the 2-D adaptive algorithm greatly reduces computational requirements and improves the learning characteristics, as compared with the direct form. The transformation filter is then generalized by including the contour parameters in the adaptive parameter set to eliminate the constraints on the frequency domain contours. It is shown how an orthogonal transformation can be included to improve the convergence rate of the constrained transformation structure