2021/03/21 by Jiaying Ren, Ren, Jiaying, Tianyi Zhang +5
Computer Science · Engineering · #Advanced Electrical Measurement Techniques #Blind Source Separation Techniques #FOS: Electrical engineering #Signal Processing (eess.SP) #Structural Health Monitoring Techniques #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2103.11500
openalex publication_date 2021/03/21 · openalex created_date 2021/03/29 · openalex updated_date 2026/07/28
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.