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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Bibliographic Details
Published in:MĀPAN : journal of Metrology Society of India Vol. 35; no. 4; pp. 547 - 554
Main Authors: Sruthikeerthi Nandita, R., Maharana, Shikha, Rajathilagam, B., Subramanya Ganesh, T., Krishnamoorthy, Subhalakshmi
Format: Journal Article
Language:English
Published: New Delhi Springer India 01.12.2020
Springer Nature B.V
Subjects:
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
Online Access:Get full text
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Summary: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.
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ISSN:0970-3950
0974-9853
DOI:10.1007/s12647-020-00414-0