An NLMS algorithm with optimized preparatory step-size parameters for SQUID-based MCG data processing

We present a new least-mean-square algorithm of adaptive filtering toimprove the signal to noise ratio for magnetocardiography data collectedwith high-temperature SQUID-based magnetometers. By frequently adjusting theadaptive parameter $alpha $ to systematic optimum values in the course ofthe programmed procedure, the convergence is accelerated with a highestspeed and the minimum steady-state error is obtained simultaneously. Thisalgorithm may be applied to eliminate other non-steady relevant noises aswell.