  We consider the problem of sinusoidal parameter estimation using signed
observations obtained via one-bit sampling with fixed as well as time-varying
thresholds. In a previous paper, a relaxation-based algorithm, referred to as
1bRELAX, has been proposed to iteratively maximize the likelihood function.
However, the exhaustive search procedure used in each iteration of 1bRELAX is
time-consuming. In this paper, we present a majorization-minimization (MM)
based 1bRELAX algorithm, referred to as 1bMMRELAX, to enhance the computational
efficiency of 1bRELAX. Using the MM technique, 1bMMRELAX maximizes the
likelihood function iteratively using simple FFT operations instead of the more
computationally intensive search used by 1bRELAX. Both simulated and
experimental results are presented to show that 1bMMRELAX can significantly
reduce the computational cost of 1bRELAX while maintaining its excellent
estimation accuracy.
