A new lattice-based adaptive notch filtering algorithm with improved mean update term

In this paper, we propose a new lattice-based adaptive notch filtering algorithm which has faster convergence characteristics than Regalia´s Simplified Lattice Algorithm (SLA). Our algorithm makes use of the weighted sum of SLA and the Lattice Gradient Algorithm. We prove that the mean update term of our algorithm is larger than that of SLA when the input signal consists of a single sinusoid and a background white noise. Furthermore, our algorithm does not change the local convergence characteristics near the sinusoidal frequency. Consequently, the proposed algorithm achieves faster convergence than SLA. A simulation result shows that the proposed algorithm finds the sinusoidal frequency faster than SLA.