Sinusoidal Parameter Estimation from Signed Measurements Obtained via Time-Varying Thresholds
Sinusoidal Parameter Estimation from Signed Measurements Obtained via Time-Varying Thresholds
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DOI：
10.1109/acssc.2018.8645123
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发表时间：
2018-10
期刊：
影响因子：
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通讯作者：
Jiaying Ren;Tianyi Zhang;Jun Yu Li;P. Stoica
中科院分区：
文献类型：
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作者：
Jiaying Ren;Tianyi Zhang;Jun Yu Li;P. Stoica
我们考虑了用时变阈值的一比特采样得到的带符号观测值来估计正弦参数的问题。在之前的一篇论文中，提出了一种基于松弛的算法，称为1bRELAX，用于迭代地最大化似然函数。然而，由于每次迭代需要耗时的穷举搜索过程，1bRELAX只能用于涉及少量正弦波的应用程序。为了提高1bRELAX的计算效率，本文提出了一种基于最大化最小化（MM）的1bRELAX算法（简称1bMMRELAX）。使用MM技术，1bMMRELAX使用简单的FFT操作迭代地最大化似然函数，从而降低了1bRELAX的计算成本，同时保持了良好的估计精度。数值算例验证了该方法的有效性。
We consider the problem of sinusoidal parameter estimation using signed observations obtained via one-bit sampling with 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, 1bRELAX can only be used in applications involving a small number of sinusoids due to the time-consuming exhaustive search procedure needed in each iteration. 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 to reduce the computational cost of 1bRELAX while maintaining its excellent estimation accuracy. Numerical examples are presented to demonstrate the effectiveness of the proposed method.