Variance estimations in the presence of intermittent interference and their applications to incoherent scatter radar signal processing

Zhou, Qihou; Li, Yanlin; Gong, Yun

doi:https://doi.org/10.5194/amt-17-4197-2024

Articles | Volume 17, issue 14

https://doi.org/10.5194/amt-17-4197-2024

© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/amt-17-4197-2024

© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 17, issue 14

Research article

|

17 Jul 2024

Research article |

| 17 Jul 2024

Variance estimations in the presence of intermittent interference and their applications to incoherent scatter radar signal processing

Qihou Zhou, Yanlin Li, and Yun Gong

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Final revised paper (published on 17 Jul 2024)
Preprint (discussion started on 14 Feb 2024)

Interactive discussion

Status: closed

RC1:
'Comment on egusphere-2024-256', Anonymous Referee #1, 27 Feb 2024

This is an interesting work. I have a minor technical comment regarding data availability. The provided link, i.e.,https://tacc.utexas.edu, does not take the reader to the raw data. Please check the link or modify the statement on data availability.

Citation: https://doi.org/10.5194/egusphere-2024-256-RC1
- AC1: 'Reply on RC1', Qihou Zhou, 28 Feb 2024
  
  A direct link is: https://tacc.utexas.edu/research/tacc-research/arecibo-observatory/
  We downloaded the data from the Arecibo Observatory before it was closed. If you can't find it in the database, we can work out a way to make the data available to you or anyone.
  Hope this helps.
  
  Citation: https://doi.org/10.5194/egusphere-2024-256-AC1
RC2:
'Comment on egusphere-2024-256', Anonymous Referee #2, 23 Apr 2024

Overall, the theoretical underpinnings of this paper are solid, leveraging established principles of robust statistics to address real-world problems in signal processing for ISR. The adaptability of these methods to handle different levels and types of interference provides a versatile toolset for other researches with data plagued by signal contamination. This approach potentially enhances the reliability of variance estimations but also contributes to the broader field of statistical signal processing by offering methods that can be adapted to applications beyond ISR.

Citation: https://doi.org/10.5194/egusphere-2024-256-RC2
- AC2: 'Reply on RC2', Qihou Zhou, 23 Apr 2024
  
  Thank you for the positive feedback.
  
  Citation: https://doi.org/10.5194/egusphere-2024-256-AC2

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload

AR by Qihou Zhou on behalf of the Authors (28 Apr 2024) Author's response Author's tracked changes Manuscript

ED: Publish as is (26 May 2024) by Meng Gao

AR by Qihou Zhou on behalf of the Authors (30 May 2024) Author's response Manuscript

Short summary

We discuss several robust estimators to compute the variance of a normally distributed random variable to deal with interference. Compared to rank-based estimators, the methods based on the geometric mean are more accurate and are computationally more efficient. We apply three robust estimators to incoherent scatter power and velocity processing, along with the traditional sample mean estimator. The best estimator is a hybrid estimator that combines the sample mean and a robust estimator.