Preprints
https://doi.org/10.5194/egusphere-2024-256
https://doi.org/10.5194/egusphere-2024-256
14 Feb 2024
 | 14 Feb 2024
Status: this preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).

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

Qihou Zhou, Yanlin Li, and Yun Gong

Abstract. We discuss robust estimations for the variance of normally distributed random variables in the presence of interferences. The robust estimators are based on either ranking or the geometric mean. For the interference models used, estimators based on the geometric mean outperform the rank-based ones both in mitigating the effect of interferences and reducing the statistical error when there is no interference. One reason for this is that estimators using the geometric mean do not suffer from the “heavy tail” phenomenon as the rank-based estimators do. The ratio of the standard deviation over the mean of the power random variable is sensitive to interference. It can thus be used to combine the sample mean with a robust estimator to form a hybrid estimator. We apply the estimators to the Arecibo incoherent scatter radar signals to determine the total power and Doppler velocities in the ionospheric E-region altitudes. Although all the robust estimators selected work well in dealing with light contaminations, the hybrid estimator is most effective in all circumstances. It performs well in suppressing heavy contaminations and is as efficient as the sample mean in reducing the statistical error. Accurate incoherent scatter radar measurements, especially at nighttime and E-region altitudes, can improve studies of ionospheric dynamics and compositions.

Qihou Zhou, Yanlin Li, and Yun Gong

Status: open (until 27 Apr 2024)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2024-256', Anonymous Referee #1, 27 Feb 2024 reply
    • AC1: 'Reply on RC1', Qihou Zhou, 28 Feb 2024 reply
Qihou Zhou, Yanlin Li, and Yun Gong
Qihou Zhou, Yanlin Li, and Yun Gong

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Short summary
We discuss several robust estimators to compute the variance of a normally distributed random variable to deal with interferences. Compared to the 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.