An Artificial Neural Network Model for Timescale Atomic Clock Ensemble Algorithm

Atomic clocks work on a standard frequency generated by the electron transitions in the atoms of the core material. A timescale is a reference frequency and phase measure generated by a set of atomic clocks. An ensemble algorithm combines the participating atomic clocks to form a “perfect” clock. Th...

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Vydáno v:	MĀPAN : journal of Metrology Society of India Ročník 35; číslo 4; s. 547 - 554
Hlavní autoři:	Sruthikeerthi Nandita, R., Maharana, Shikha, Rajathilagam, B., Subramanya Ganesh, T., Krishnamoorthy, Subhalakshmi
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New Delhi Springer India 01.12.2020 Springer Nature B.V
Témata:	Algorithms Artificial neural networks Atomic clocks Clocks & watches Deviation Electron transitions Kalman filters Mathematical and Computational Physics Mathematical Methods in Physics Measurement Science and Instrumentation Neural networks Numerical and Computational Physics Original Paper Physics Physics and Astronomy Simulation Theoretical Time Atomic Clocks Frequency stability analysis Indian regional navigation satellite system (IRNSS) Allan deviation Artificial neural networks IRNSS Network Timing Centre (IRNWT) Navigation with Indian Constellation (NavIC) Hadamard deviation
ISSN:	0970-3950, 0974-9853
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Abstract	Atomic clocks work on a standard frequency generated by the electron transitions in the atoms of the core material. A timescale is a reference frequency and phase measure generated by a set of atomic clocks. An ensemble algorithm combines the participating atomic clocks to form a “perfect” clock. The perfect clock is very stable and precise in terms of frequency and phase. There are many methods that exist to develop an ensemble for a timescale such as Kalman filter-based algorithms, inverse Allan variance-based algorithms, etc. A neural network-based realization of the ensemble algorithm for a timescale is discussed in this paper. The artificial neural network (ANN) model dynamically adapts the weights of the clocks to accommodate the behavioural changes in the clocks. This paper uses different types of M-sample deviations like overlapping Allan deviation and overlapping Hadamard deviation as the inputs to the model.
AbstractList	Atomic clocks work on a standard frequency generated by the electron transitions in the atoms of the core material. A timescale is a reference frequency and phase measure generated by a set of atomic clocks. An ensemble algorithm combines the participating atomic clocks to form a “perfect” clock. The perfect clock is very stable and precise in terms of frequency and phase. There are many methods that exist to develop an ensemble for a timescale such as Kalman filter-based algorithms, inverse Allan variance-based algorithms, etc. A neural network-based realization of the ensemble algorithm for a timescale is discussed in this paper. The artificial neural network (ANN) model dynamically adapts the weights of the clocks to accommodate the behavioural changes in the clocks. This paper uses different types of M-sample deviations like overlapping Allan deviation and overlapping Hadamard deviation as the inputs to the model.
Author	Krishnamoorthy, Subhalakshmi Subramanya Ganesh, T. Sruthikeerthi Nandita, R. Maharana, Shikha Rajathilagam, B.
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Keywords	Atomic Clocks Frequency stability analysis Indian regional navigation satellite system (IRNSS) Allan deviation Artificial neural networks IRNSS Network Timing Centre (IRNWT) Navigation with Indian Constellation (NavIC) Hadamard deviation
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References_xml	– reference: Ahn, In Soo, “State modeling of clock noises and its application” (1986). Retrospective Theses and Dissertations. 8053. https://lib.dr.iastate.edu/rtd/8053. – reference: W. Riley, and D.A. Howe, NIST Handbook of Frequency Stability Analysis. NIST Special Publication, 1065. 20899 (2008). – reference: C. A. Greenhall, Likelihood and Least-squares Approach to M-Cornered Hat. PORC 1987 PTTI Meeting 9 (1987). – reference: P. Sherman, “Allan Variance” Iowa State University. http://home.engineering.iastate.edu/~shermanp/AERE432/lectures/Rate%20Gyros/Allan%20variance.pdf. – reference: D.W. Allan, Historicity, Strengths, and Weaknesses of Allan Variances and their General Applications. Gyroscopy Navig. (2016). – reference: BhardwajanAMaharanaSGaneshTA Comparative Study of Methods of Clock Ensemble DevelopmentMAPAN201710.1007/s12647-016-0197-x – reference: S. Podogova, K. Mishagin, Frequency Combining System for Atomic Clock Ensembles. (2014) https://doi.org/10.1109/eftf.2014.7331564. – reference: J. Nelson, “What is an Atomic Clock?” nasa.gov. http://www.nasa.gov/feature/jpl/what-is-an-atomic-clock. – reference: MaharanaSBhardwajanAGaneshTRamakrishnaBTimescale Ensemble Performance Enhancement Through Use of Artificial Neural NetworkINCOSE Int. Symp.20192947848810.1002/j.2334-5837.2019.00701.x – reference: W.J. Riley, “3-corn Hat” wriley.com. http://www.wriley.com/3-CornHat.htm. Accessed 7 March 2020. – ident: 414_CR10 doi: 10.1109/eftf.2014.7331564 – year: 2017 ident: 414_CR7 publication-title: MAPAN doi: 10.1007/s12647-016-0197-x – ident: 414_CR9 – ident: 414_CR2 – volume: 29 start-page: 478 year: 2019 ident: 414_CR3 publication-title: INCOSE Int. Symp. doi: 10.1002/j.2334-5837.2019.00701.x – ident: 414_CR5 doi: 10.1134/S2075108716010028 – ident: 414_CR8 – ident: 414_CR1 – ident: 414_CR6 – ident: 414_CR4
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SubjectTerms	Algorithms Artificial neural networks Atomic clocks Clocks & watches Deviation Electron transitions Kalman filters Mathematical and Computational Physics Mathematical Methods in Physics Measurement Science and Instrumentation Neural networks Numerical and Computational Physics Original Paper Physics Physics and Astronomy Simulation Theoretical Time
Title	An Artificial Neural Network Model for Timescale Atomic Clock Ensemble Algorithm
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