Articles | Volume 15, issue 12
https://doi.org/10.5194/amt-15-3843-2022
https://doi.org/10.5194/amt-15-3843-2022
Research article
 | 
28 Jun 2022
Research article |  | 28 Jun 2022

Hierarchical deconvolution for incoherent scatter radar data

Snizhana Ross, Arttu Arjas, Ilkka I. Virtanen, Mikko J. Sillanpää, Lassi Roininen, and Andreas Hauptmann

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Cited articles

Adler, J. and Öktem, O.: Deep bayesian inversion, arXiv [preprint], arXiv:1811.05910, 14 November 2018. a
Arjas, A.: Hierarchical-deconvolution: Hierarchical deconvolution codes, Version V1, Zenodo [code], https://doi.org/10.5281/zenodo.6542699, 2022. a, b
Arjas, A., Hauptmann, A., and Sillanpää, M. J.: Estimation of dynamic SNP-heritability with Bayesian Gaussian process models, Bioinformatics, 36, 3795–3802, https://doi.org/10.1093/bioinformatics/btaa199, 2020a. a
Arjas, A., Roininen, L., Sillanpää, M. J., and Hauptmann, A.: Blind hierarchical deconvolution, in: 2020 IEEE 30th International Workshop on Machine Learning for Signal Processing (MLSP), IEEE, 1–6, https://doi.org/10.1109/MLSP49062.2020.9231822, 2020b. a, b, c
Barker, R. H.: Group synchronizing of binary digital systems, in: Communication Theory, edited by: Jackson, W., Academic Press, New York, 273–287, 1953. a
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Short summary
Radar measurements of thermal fluctuations in the Earth's ionosphere produce weak signals, and tuning to specific altitudes results in suboptimal resolution for other regions, making an accurate analysis of these changes difficult. A novel approach to improve the resolution and remove measurement noise is considered. The method can capture variable characteristics, making it ideal for the study of a large range of data. Synthetically generated examples and two measured datasets were considered.
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