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Asian Journal of Scientific Research
  Year: 2019 | Volume: 12 | Issue: 1 | Page No.: 97-104
DOI: 10.3923/ajsr.2019.97.104
 
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Mesosphere-Stratosphere-Troposphere Radar Data Processing using Sparse Learning via Iterative Minimization
C. Raju and T. Sreenivasulu Reddy

Abstract:
Background and Objective: The Atmospheric Radars can provide accurate wind parameters using various spectral estimation techniques. Existing methods for spectrum estimation, however, often fail to detect the signal at low signal-to-noise ratio (SNR) conditions and to estimate precise wind parameters. In this study, a regularized minimization approach, Sparse Learning via Iterative Minimization (SLIM) is considered for the spectral analysis. Methodology: SLIM, which is a high resolution semiparametric adaptive algorithm, follows an lq-norm based minimization method for sparse signal and noise power estimation. This is applied for atmospheric data collected at National Atmospheric Research Laboratory (NARL), Gadanki, India, from the Mesosphere-Stratosphere-Troposphere (MST) radar, backscattered echoes. Results: The results show that SLIM gives a better SNR or high detectability. The Zonal, Meridional, Wind speeds are calculated, and validated using the real-time Global Positioning System (GPS) Sonde data. Conclusion: It can be concluded that SLIM has better performance when compared to the previous methods. The correlation between the wind speeds computed using GPS and SLIM for the radar data collected in February 2015 has a correlation factor of 0.94.
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How to cite this article:

C. Raju and T. Sreenivasulu Reddy, 2019. Mesosphere-Stratosphere-Troposphere Radar Data Processing using Sparse Learning via Iterative Minimization. Asian Journal of Scientific Research, 12: 97-104.

DOI: 10.3923/ajsr.2019.97.104

URL: https://scialert.net/abstract/?doi=ajsr.2019.97.104

 
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