APPLICATION OF ORTHONORMAL TRANSFORMS TO IMPLEMENTATION OF QUASI-LMS NEWTON ALGORITHM

An efficient implementation of the LMS/Newton (LMSN) algorithm is proposed. The resulting scheme is quasi in the sense that a quasi-inverse of the correlation matrix of input is used, instead of its exact inverse. The proposed algorithm, which is an alternative formulation of a class of the existing transform domain adaptive filters (TDAF's), has some advantages over the TDAF. An eminent feature of the proposed scheme that greatly simplifies its implementation is the possibility of coarse quantization of the stochastic gradient terms in the adjustment recursion. To back this up, an analysis of the LMSN algorithm, which includes the effect of coarse quantization of the stochastic gradient terms, is given for correlated Gaussian data. Computer simulations that support the developed theories are also presented.