基于步长控制的微波测量频率采样算法

C. Rosca, N. Paraschiv
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引用次数: 1

摘要

将微波频率测量从极低频率扩展到极高频率,需要改进采集时间。本文提出了一种新的采样算法,其主要目的是利用有限的采样数来减少采集时间。本文提出的自适应算法只计算有限数量的样本,然后利用插值模型重建整个电路的响应。该方法采用自适应步长控制,并预先定义了初始步长和误差。该算法评估两个连续S参数之间的差值。自适应步长算法假设,当当前S参数值与前一个S参数值之间的距离减小(小于一个阈值)时,可以将探索步长增加到一个极限,以使步长保持在一个适中的值。否则,它可能会忽略S参数的主要变化。这里最大的挑战是S参数域、频域和标度值之间的相关性。该算法能自动找到准确评估高S参数变化所需的点数,计算速度快。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Frequency sampling algorithm applied in microwave measurements based on step-size control method
Extending the microwave frequency measurement from very low to very high frequency requires improvements for the acquisition time. In this paper, a new sampling algorithm is presented with the main purpose to reduce the acquisition time by using a limited number of samples. The adaptive algorithm proposed in this paper computes only a limited number of samples and then reconstructs the entire circuit response using the interpolation model. This method uses an adaptive step-size control and has the initial step and the error predefined. The algorithm evaluates the difference between two consecutive S parameters. The adaptive step-size algorithm assumes that, when the distance between the current S parameter value and the previous one decreases (below a threshold ∊), the exploration step-size may be increased up to a limit in order to keep the step-size to a moderate value. Otherwise, it might overlook major S parameter variations. The biggest challenge here is represented by the correlation between S parameter domain, frequency domain and scaling values. This algorithm automatically finds the number of points needed to accurately evaluate high S parameters variations and it has a high speed computing.
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