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 to improve the signal to noise ratio for magnetocardiography data collected with high-temperature SQUID-based magnetometers. By frequently adjusting the adaptive parameter $\alpha $ 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.