LMS-based notch filter for the estimation of sinusoidal signals in noise
Signal Processing
Understanding Smart Sensors, Second Edition
Understanding Smart Sensors, Second Edition
Step size bound of the sequential partial update LMS algorithm with periodic input signals
EURASIP Journal on Audio, Speech, and Music Processing
Logarithmic quantization in the least mean squares algorithm
Digital Signal Processing
An optimum algorithm for adaptive filtering on acoustic echo cancellation using TMS320C6713 DSP
Digital Signal Processing
A modified Armijo rule for the online selection of learning rate of the LMS algorithm
Digital Signal Processing
Channel equalization using simplified least mean-fourth algorithm
Digital Signal Processing
An adaptive estimation of periodic signals using a Fourier linearcombiner
IEEE Transactions on Signal Processing
H∞ optimality of the LMS algorithm
IEEE Transactions on Signal Processing
On the design of LMS-based channel estimators using the Doppler spread parameter
Digital Signal Processing
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The response of the Least Mean Square (LMS) algorithm to deterministic periodic inputs is considered. Under these conditions, initial values of the tap-weight vector can be identified that lead to periodic responses of LMS filters. The stability of these periodic responses determines the long-term convergence of the filter. This analysis presents some advantages over the classical studies based on the correlation matrix, because it leads to more accurate results and a better understanding of the filter operation. It is also shown that such an operation does not change essentially for more realistic inputs, as when the desired response is perturbed with a zero-mean random signal. Finally, to validate the obtained results, some simulations and experiments have been conducted for an adaptive noise canceller.