Algorithm for Generating the Output Signal of a Group Frequency Reference

Mishagin, K. G.; Podogova, S. D.; Chernyshev, I. N.; Medvedev, S. Yu.

doi:10.1007/s11018-013-0302-2

Cited by 5 publications

(5 citation statements)

References 4 publications

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“…Unlike the Kalman fi lter, it does not require information about the model of the random process and its parameters for describing the atomic clock signals. According to the simulation results presented in [13], the Allan deviation of the group signal generated by this algorithm is close to the minimum possible values for the entire range of measurement time intervals considered. Thus, the control algorithm has to be comparable in stability characteristics of the formed weighted average frequency to the presently known algorithms for the…”

supporting

confidence: 56%

“…However, their operation requires a priori information about the parameters of the phase noise of atomic clock signals; moreover, the Kalman fi lter signifi cantly complicates accounting for fl icker frequency noise [12]. We also note that the computational complexity of algorithms based on the Kalman fi lter increases nonlinearly with increasing number of atomic clocks in the ensemble, and the procedure for adding or excluding an atomic clock from the group turns out to be more complicated than in other methods.Previously there was proposed an algorithm using a frequency-locked loop with an auxiliary generator relative to an ensemble of quantum clocks to form the group signal [13]. This algorithm uses the moving average method to estimate the fl uctuation frequency of the input signals over different time intervals.…”

mentioning

confidence: 99%

“…Previously there was proposed an algorithm using a frequency-locked loop with an auxiliary generator relative to an ensemble of quantum clocks to form the group signal [13]. This algorithm uses the moving average method to estimate the fl uctuation frequency of the input signals over different time intervals.…”

mentioning

confidence: 99%

“…Note that the choice of parameters τ i w , τ i in METS (VCH) is performed in the same way as was done in the frequency control algorithm for an auxiliary generator [13]. The intervals τ i w used to calculate the weight coeffi cients must be determined by the requirements of the standard: on which measurement time intervals is it more important to obtain minimal frequency instability values for the group time scale.…”

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An Algorithm for a Group Time Scale Using a Moving Average Over Multiple Time Scales

et al. 2015

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A new algorithm is presented for the formation of an analytical time scale that takes into account the fl uctuations of atomic clock signal frequencies over several time scales. Numerical simulation shows that the algorithm can obtain a group time scale for an ensemble of atomic clocks with rather high frequency stability characteristics. The proposed algorithm is compared with the algorithm for calculating a time scale based on the Kalman fi lter. Analytical time scales are widely used in time and frequency standards based on an ensemble of atomic clocks to increase precision and stability characteristics. At this time, there are a large number of algorithms for computing analytic time scales. Among them there are algorithms which process results a posteriori and algorithms in which the corrections for atomic clock signals constituting a reference group are calculated in real time. The most frequently used computational methods are various modifi cations of the basic equations of the time scale (ALGOS, FAT, AT1, METS) [1-6], Kalman fi ltering (Kred, IEM) [7-9], as well as their combination (KAS-2, KPW) [10 -11]. Algorithms based on the Kalman fi lter enable a suffi ciently precise estimate of signal frequency and phase; as a rule, they give better results compared with algorithms based only on the basic equations of the time scale, such as AT1 and ALGOS. However, their operation requires a priori information about the parameters of the phase noise of atomic clock signals; moreover, the Kalman fi lter signifi cantly complicates accounting for fl icker frequency noise [12]. We also note that the computational complexity of algorithms based on the Kalman fi lter increases nonlinearly with increasing number of atomic clocks in the ensemble, and the procedure for adding or excluding an atomic clock from the group turns out to be more complicated than in other methods.Previously there was proposed an algorithm using a frequency-locked loop with an auxiliary generator relative to an ensemble of quantum clocks to form the group signal [13]. This algorithm uses the moving average method to estimate the fl uctuation frequency of the input signals over different time intervals. Unlike the Kalman fi lter, it does not require information about the model of the random process and its parameters for describing the atomic clock signals. According to the simulation results presented in [13], the Allan deviation of the group signal generated by this algorithm is close to the minimum possible values for the entire range of measurement time intervals considered. Thus, the control algorithm has to be comparable in stability characteristics of the formed weighted average frequency to the presently known algorithms for the

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confidence: 99%

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confidence: 99%

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confidence: 99%

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An Algorithm for a Group Time Scale Using a Moving Average Over Multiple Time Scales

et al. 2015

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“…[5], Filimonov B. P., Khrustalev Yu. P. [6], Tolstikov A. S. According to the results of researches executed in various organizations, use of the predictive models allows to increase the accuracy of state estimation of standards of time and frequency (ensemble of atomic clock) by 20-30% [6,7,8].…”

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confidence: 99%

Construction of Predictive Models of Systems with Incomplete Observation Matrix

Serysheva¹,

Khrustalev²

2019

Proceedings of the VIth International Workshop 'Critical Infrastructures: Contingency Management, Intelligent, Agent-Based, Clo

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The formalized technique of processing the measurement information obtained in the subsystem of internal comparisons of the standard based on the results of indirect measurements performed in the process of the functioning of the group standard of time and frequencies is offered. The program modules realizing a technique allow to solve a problem of full automation of process of creation of models of time series according to empirical data and to lower an error of reproduction of units of time and frequency by atomic clock to 30%. Keywordsgroup standards of time and frequency (ensemble of atomic clocks), predictive models of time series, filtration of anomalous measurements, regression analysis, extremum of function of many variables.

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