Convergence models for adaptive gradient and least squares algorithms

A simple model characterizing the convergence properties of an adaptive digital lattice filter using gradient algorithms has been reported [1]. This model is extended to the least mean square (LMS) lattice joint process estimator, to the recursive least squares (LS) algorithms, and is compared with computer simulations. Interestingly, the LS models are more accurate than the previous LMS models. In addition, although the LS lattice consistently converges somewhat faster than the LMS lattice, they both exhibit similar behavior.