{"title":"基于自适应频率标准跟踪的高精度频率检测与分析","authors":"Baoqiang Du, Zhengze Xiao, Lanqin Tan","doi":"10.1049/sil2/8914468","DOIUrl":null,"url":null,"abstract":"<div>\n <p>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<sup>−13</sup>/s.</p>\n </div>","PeriodicalId":56301,"journal":{"name":"IET Signal Processing","volume":"2025 1","pages":""},"PeriodicalIF":1.1000,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/sil2/8914468","citationCount":"0","resultStr":"{\"title\":\"High-Accuracy Frequency Detection and Analysis via Adaptive Frequency Standard Tracking\",\"authors\":\"Baoqiang Du, Zhengze Xiao, Lanqin Tan\",\"doi\":\"10.1049/sil2/8914468\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div>\\n <p>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<sup>−13</sup>/s.</p>\\n </div>\",\"PeriodicalId\":56301,\"journal\":{\"name\":\"IET Signal Processing\",\"volume\":\"2025 1\",\"pages\":\"\"},\"PeriodicalIF\":1.1000,\"publicationDate\":\"2025-04-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://onlinelibrary.wiley.com/doi/epdf/10.1049/sil2/8914468\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IET Signal Processing\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1049/sil2/8914468\",\"RegionNum\":4,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IET Signal Processing","FirstCategoryId":"5","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1049/sil2/8914468","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
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.
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