Abstract
We present a new least-mean-square algorithm of adaptive filtering to improve the signal to noise ratio for magnetocardiography data collected with high-temperature SQUID-based magnetometers. By frequently adjusting the adaptive parameter α to systematic optimum values in the course of the programmed procedure, the convergence is accelerated with a highest speed and the minimum steady-state error is obtained simultaneously. This algorithm may be applied to eliminate other non-steady relevant noises as well.
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