A least squares algorithm is presented which uses one weight for phase shift correction per reference input for adaptive noise canceling with one or multiple reference inputs. The method uses an iterative gradient search procedure which assumes that phase shifts between inputs are small compared to the frequency of meaningful, correlated, periodic noise components. In a simulation in which the primary signal contained additive, correlated sinusoidal and random noise, the weights converged stably and rapidly to the minimum of the performance surface. The algorithm is potentially useful in real-time medical applications, where minimizing the cost of implementation is essential.
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