An adaptive filtering algorithm with parameter acceleration

We introduce a novel fast discrete-time algorithm for adaptive filtering applications. It is based on a tuner used in adaptive control that sets the second derivative of the parameter estimates. For colored input signals the proposed algorithm presents a more favorable compromise between convergence speed and steady-state estimation error than the LMS or NLMS algorithms, a property obtained at the cost of a moderate increase in computational complexity. We provide a deterministic convergence proof and present simulation results