Hierarchical Deconvolution for Incoherent Scatter Radar Data
We propose a novel method for deconvolving incoherent scatter radar data to recover accurate reconstructions of backscattered powers. The problem is modelled as a hierarchical noise-perturbed deconvolution problem, where the lower hierarchy consists of an adaptive length-scale function that allows f...
Main Authors: | , , , , , |
---|---|
Format: | Text |
Language: | English |
Published: |
2021
|
Subjects: | |
Online Access: | https://doi.org/10.5194/amt-2021-287 https://amt.copernicus.org/preprints/amt-2021-287/ |
id |
ftcopernicus:oai:publications.copernicus.org:amtd97870 |
---|---|
record_format |
openpolar |
spelling |
ftcopernicus:oai:publications.copernicus.org:amtd97870 2023-05-15T16:04:45+02:00 Hierarchical Deconvolution for Incoherent Scatter Radar Data Ross, Snizhana Arjas, Arttu Virtanen, Ilkka I. Sillanpää, Mikko J. Roininen, Lassi Hauptmann, Andreas 2021-09-21 application/pdf https://doi.org/10.5194/amt-2021-287 https://amt.copernicus.org/preprints/amt-2021-287/ eng eng doi:10.5194/amt-2021-287 https://amt.copernicus.org/preprints/amt-2021-287/ eISSN: 1867-8548 Text 2021 ftcopernicus https://doi.org/10.5194/amt-2021-287 2021-09-27T16:22:26Z We propose a novel method for deconvolving incoherent scatter radar data to recover accurate reconstructions of backscattered powers. The problem is modelled as a hierarchical noise-perturbed deconvolution problem, where the lower hierarchy consists of an adaptive length-scale function that allows for a non-stationary prior and as such enables adaptive recovery of smooth and narrow layers in the profiles. The estimation is done in a Bayesian statistical inversion framework as a two-step procedure, where hyperparameters are first estimated by optimisation and followed by an analytical closed-form solution of the deconvolved signal. The proposed optimisation based method is compared to a fully probabilistic approach using Markov Chain Monte Carlo techniques enabling additional uncertainty quantification. In this paper we examine the potential of the hierarchical deconvolution approach using two different prior models for the length-scale function.We apply the developed methodology to compute the backscattered powers of measured Polar MesosphericWinter Echoes, as well as Summer Echoes, from the EISCAT VHF radar in Tromsø, Norway. Computational accuracy and performance are tested using a simulated signal corresponding to a typical background ionosphere and a sporadic E layer with known ground-truth. The results suggest that the proposed hierarchical deconvolution approach can recover accurate and clean reconstructions of profiles, and the potential to be successfully applied to similar problems. Text EISCAT Tromsø Copernicus Publications: E-Journals Norway Tromsø |
institution |
Open Polar |
collection |
Copernicus Publications: E-Journals |
op_collection_id |
ftcopernicus |
language |
English |
description |
We propose a novel method for deconvolving incoherent scatter radar data to recover accurate reconstructions of backscattered powers. The problem is modelled as a hierarchical noise-perturbed deconvolution problem, where the lower hierarchy consists of an adaptive length-scale function that allows for a non-stationary prior and as such enables adaptive recovery of smooth and narrow layers in the profiles. The estimation is done in a Bayesian statistical inversion framework as a two-step procedure, where hyperparameters are first estimated by optimisation and followed by an analytical closed-form solution of the deconvolved signal. The proposed optimisation based method is compared to a fully probabilistic approach using Markov Chain Monte Carlo techniques enabling additional uncertainty quantification. In this paper we examine the potential of the hierarchical deconvolution approach using two different prior models for the length-scale function.We apply the developed methodology to compute the backscattered powers of measured Polar MesosphericWinter Echoes, as well as Summer Echoes, from the EISCAT VHF radar in Tromsø, Norway. Computational accuracy and performance are tested using a simulated signal corresponding to a typical background ionosphere and a sporadic E layer with known ground-truth. The results suggest that the proposed hierarchical deconvolution approach can recover accurate and clean reconstructions of profiles, and the potential to be successfully applied to similar problems. |
format |
Text |
author |
Ross, Snizhana Arjas, Arttu Virtanen, Ilkka I. Sillanpää, Mikko J. Roininen, Lassi Hauptmann, Andreas |
spellingShingle |
Ross, Snizhana Arjas, Arttu Virtanen, Ilkka I. Sillanpää, Mikko J. Roininen, Lassi Hauptmann, Andreas Hierarchical Deconvolution for Incoherent Scatter Radar Data |
author_facet |
Ross, Snizhana Arjas, Arttu Virtanen, Ilkka I. Sillanpää, Mikko J. Roininen, Lassi Hauptmann, Andreas |
author_sort |
Ross, Snizhana |
title |
Hierarchical Deconvolution for Incoherent Scatter Radar Data |
title_short |
Hierarchical Deconvolution for Incoherent Scatter Radar Data |
title_full |
Hierarchical Deconvolution for Incoherent Scatter Radar Data |
title_fullStr |
Hierarchical Deconvolution for Incoherent Scatter Radar Data |
title_full_unstemmed |
Hierarchical Deconvolution for Incoherent Scatter Radar Data |
title_sort |
hierarchical deconvolution for incoherent scatter radar data |
publishDate |
2021 |
url |
https://doi.org/10.5194/amt-2021-287 https://amt.copernicus.org/preprints/amt-2021-287/ |
geographic |
Norway Tromsø |
geographic_facet |
Norway Tromsø |
genre |
EISCAT Tromsø |
genre_facet |
EISCAT Tromsø |
op_source |
eISSN: 1867-8548 |
op_relation |
doi:10.5194/amt-2021-287 https://amt.copernicus.org/preprints/amt-2021-287/ |
op_doi |
https://doi.org/10.5194/amt-2021-287 |
_version_ |
1766400377180127232 |