Sinusoidal Parameter Estimation From Signed Measurements Via Majorization–Minimization Based RELAX
Sinusoidal Parameter Estimation From Signed Measurements Via Majorization–Minimization Based RELAX
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DOI：
10.1109/tsp.2019.2899804
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发表时间：
2019-02
影响因子：
5.4
通讯作者：
Jiaying Ren;Tianyi Zhang;Jian Li;P. Stoica
中科院分区：
文献类型：
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作者：
Jiaying Ren;Tianyi Zhang;Jian Li;P. Stoica
我们考虑的问题，正弦参数估计使用签署的意见，通过一位采样固定以及随时间变化的阈值。在以前的论文中，一个基于松弛的算法，称为1bRELAX，已被提出迭代最大化的似然函数。然而，在1bRELAX的每次迭代中使用的穷举搜索过程是耗时的。在本文中，我们提出了一个优化最小化（MM）为基础的1bRELAX算法，简称1bMMRELAX，以提高计算效率的1bRELAX。使用MM技术，1bMMRELAX使用简单的快速傅立叶变换操作迭代地最大化似然函数，而不是1bRELAX使用的计算密集型搜索。仿真和实验结果表明，1bMMRELAX可以显着降低计算成本的1bRELAX，同时保持其优良的估计精度。
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 fast Fourier transform 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.