基于自适应频率标准跟踪的高精度频率检测与分析

IF 1.1 4区 工程技术 Q4 ENGINEERING, ELECTRICAL & ELECTRONIC
Baoqiang Du, Zhengze Xiao, Lanqin Tan
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引用次数: 0

摘要

精确频率检测是高精度时频传递中需要解决的关键问题之一。该问题的解决有助于提高相位噪声测量、原子频率标准和时间同步的精度,在整个精密测量物理领域具有重要作用。针对无需频率归一化的时频信号处理,提出了一种基于自适应频率标准跟踪的高精度频率检测与分析方法。首先,基于测量信号,利用FPGA控制DDS产生自适应频率标准信号。该信号可以在任何频率关系下实现与被测信号的相位比较,包括复频差关系和大频差关系,拓宽了频率测量范围。其次,对频率标准信号进行延时处理。粗糙的时间延迟可以产生许多相位重合,从而缩短栅极开关时间,实现快速的时间响应。更细的延迟可以提供非常高的测量分辨率,而不会改变测量信号和参考信号之间的频率关系。然后,对测量信号和参考信号进行整形和调理后,进行差分同步。得到的最优相位重合,即模糊带边缘脉冲,作为门信号。然后,通过对门时间内无间隙的测量信号和参考信号进行计数,可以实现对被测信号的精确频率测量。测试结果表明,该系统的测频精度可达1.7 × 10−13/s。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

High-Accuracy Frequency Detection and Analysis via Adaptive Frequency Standard Tracking

High-Accuracy Frequency Detection and Analysis via Adaptive Frequency Standard Tracking

Precise frequency detection is one of the key problems to be solved in a high-accuracy transfer of time and frequency. The solution to this problem is helpful in improving the precision of the phase noise measurement, atomic frequency standard, and time synchronization, which plays a strong role in the whole precision measurement physics fields. A high-accuracy frequency detection and analysis based on adaptive frequency standard tracking are proposed for time–frequency signal processing without frequency normalization. First, an adaptive frequency standard signal is generated by using an FPGA to control the DDS based on the measured signal. This signal can achieve phase comparison with the measured signal under any frequency relationships including complex and large-frequency difference relationships, widening a frequency measurement range. Second, the frequency standard signal is put off by the delay chains. The rough time delaying can generate many phase coincidences, which can shorten the gate switch time to achieve fast time response. The finer delaying can provide a very high measurement resolution without transforming the frequency relationships between the measured and reference signals. And then, a differential synchronization is performed between the measured and reference signals after shaping and conditioning the two signals. The obtained optimal phase coincidences, that is, fuzzy zone edge pulses, are used as the gate signals. A precise frequency measurement for the measured signals can then be realized by counting the measured and reference signals without gap in the gate time. The testing results show that the frequency measurement accuracy of the system can reach 1.7 × 10−13/s.

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来源期刊
IET Signal Processing
IET Signal Processing 工程技术-工程:电子与电气
CiteScore
3.80
自引率
5.90%
发文量
83
审稿时长
9.5 months
期刊介绍: IET Signal Processing publishes research on a diverse range of signal processing and machine learning topics, covering a variety of applications, disciplines, modalities, and techniques in detection, estimation, inference, and classification problems. The research published includes advances in algorithm design for the analysis of single and high-multi-dimensional data, sparsity, linear and non-linear systems, recursive and non-recursive digital filters and multi-rate filter banks, as well a range of topics that span from sensor array processing, deep convolutional neural network based approaches to the application of chaos theory, and far more. Topics covered by scope include, but are not limited to: advances in single and multi-dimensional filter design and implementation linear and nonlinear, fixed and adaptive digital filters and multirate filter banks statistical signal processing techniques and analysis classical, parametric and higher order spectral analysis signal transformation and compression techniques, including time-frequency analysis system modelling and adaptive identification techniques machine learning based approaches to signal processing Bayesian methods for signal processing, including Monte-Carlo Markov-chain and particle filtering techniques theory and application of blind and semi-blind signal separation techniques signal processing techniques for analysis, enhancement, coding, synthesis and recognition of speech signals direction-finding and beamforming techniques for audio and electromagnetic signals analysis techniques for biomedical signals baseband signal processing techniques for transmission and reception of communication signals signal processing techniques for data hiding and audio watermarking sparse signal processing and compressive sensing Special Issue Call for Papers: Intelligent Deep Fuzzy Model for Signal Processing - https://digital-library.theiet.org/files/IET_SPR_CFP_IDFMSP.pdf
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