Uncorrelated Weighted Median Filtering for Noise Removal in SuperDARN

Goh, Clarence; Devlin, John; Deng, Dennis; McDonald, Andrew J.; Kamarudin, Muhammad Ramlee

doi:10.1109/cse.2014.196

Cited by 2 publications

(1 citation statement)

References 10 publications

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“…We have not considered contributions of noise as it is a simple exercise to add this later [e.g., Farley , ]. The noise in SuperDARN voltage samples is Gaussian [see Goh et al , , Figure 8], so the noise contribution to the voltage samples is accounted for by adding the variance of the noise,

σ_{N}^{2}

, to the main diagonal of C . Equation shows that estimates of the mean ACF are given by the sum of random variables (the voltage samples, V 1 and V 2 , are random variables, and therefore, their products are random variables).…”

Section: Mean Acf Estimate Statisticsmentioning

confidence: 99%

On the statistics of SuperDARN autocorrelation function estimates

2016

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Time domain signal processing techniques are employed by the Super Dual Auroral Radar Network (SuperDARN) to obtain bulk measurements of the velocity and spectral width of F region ionospheric plasma irregularities. The measurements are obtained by fitting estimates of the mean autocorrelation function (ACF) of the radar target. To accurately and consistently extract target parameters from the mean unnormalized ACF, it is necessary to utilize error‐weighted fitting algorithms with a weight given by the variance of the ACF. Currently implemented weights are ad hoc, and a detailed description of the statistical characterization of SuperDARN ACFs is needed. Following the discussions in Farley (1969) and Woodman and Hagfors (1969), which describe the variance for the mean normalized ACF used with incoherent scatter radars, we present analytic expressions for obtaining the variance of the real and imaginary components of the mean unnormalized SuperDARN ACF. These expressions are based on models by André et al. (1999) and Moorcroft (2004) of the voltage signal received by SuperDARN radars but may be used for other soft target radar systems. An algorithm for obtaining the variance of both the magnitude and phase of the mean ACF is also presented. The results of this study may be directly integrated into existing SuperDARN data analysis software and other pulse‐Doppler radar systems that utilize estimates of the mean unnormalized ACF.

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σ_{N}^{2}

Section: Mean Acf Estimate Statisticsmentioning

confidence: 99%

On the statistics of SuperDARN autocorrelation function estimates

2016

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Zhongshan HF Radar Elevation Calibration Based on Ground Backscatter Echoes

et al. 2022

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The super dual auroral radar network (SuperDARN) is an important tool in the remote sensing of ionospheric potential convection in middle and high latitudes, and also a major source of elevation data detection. A reliable elevation angle helps estimate the propagation paths of high-frequency radio signals between scattering spots and radars, which is crucial for determining high-frequency radar target geolocation. The SuperDARN radar uses interferometry to estimate the elevation of the returned signal. However, elevation data are still underutilized owing to the difficulties of phase difference calibration induced by the propagation time delay between two arrays. This paper statistically analyzes the distribution features of the group range-elevation angle and group range-virtual height before and after calibration using elevation data from the ground backscatter echoes of the Zhongshan SuperDARN radar, calculating the root mean square error (RMSE) of the virtual height; the results show that the RMSE after calibration is mostly reduced to within 54% of that before calibration. Furthermore, we validate the calibration factor based on the primary phase data. The data from 2013 to 2015 indicate that this technique can be efficiently used to estimate the daily calibration factor. Finally, we present the statistical distribution of the calibration factor, which provides technical support for the calibration of elevation data in the future.

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Uncorrelated Weighted Median Filtering for Noise Removal in SuperDARN

Cited by 2 publications

References 10 publications

On the statistics of SuperDARN autocorrelation function estimates

On the statistics of SuperDARN autocorrelation function estimates

Zhongshan HF Radar Elevation Calibration Based on Ground Backscatter Echoes

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