M.Anitha 1
, J. Avanija2
Section:Research Paper, Product Type: Journal Paper
Volume-06 ,
Issue-04 , Page no. 205-208, May-2018
Online published on May 31, 2018
Copyright © M.Anitha, J. Avanija . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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IEEE Style Citation: M.Anitha, J. Avanija , “Implementing PCA on MST Radar data for Wind Analysis,” International Journal of Computer Sciences and Engineering, Vol.06, Issue.04, pp.205-208, 2018.
MLA Style Citation: M.Anitha, J. Avanija "Implementing PCA on MST Radar data for Wind Analysis." International Journal of Computer Sciences and Engineering 06.04 (2018): 205-208.
APA Style Citation: M.Anitha, J. Avanija , (2018). Implementing PCA on MST Radar data for Wind Analysis. International Journal of Computer Sciences and Engineering, 06(04), 205-208.
BibTex Style Citation:
@article{Avanija_2018,
author = {M.Anitha, J. Avanija },
title = {Implementing PCA on MST Radar data for Wind Analysis},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {5 2018},
volume = {06},
Issue = {04},
month = {5},
year = {2018},
issn = {2347-2693},
pages = {205-208},
url = {https://www.ijcseonline.org/full_spl_paper_view.php?paper_id=382},
publisher = {IJCSE, Indore, INDIA},
}
RIS Style Citation:
TY - JOUR
UR - https://www.ijcseonline.org/full_spl_paper_view.php?paper_id=382
TI - Implementing PCA on MST Radar data for Wind Analysis
T2 - International Journal of Computer Sciences and Engineering
AU - M.Anitha, J. Avanija
PY - 2018
DA - 2018/05/31
PB - IJCSE, Indore, INDIA
SP - 205-208
IS - 04
VL - 06
SN - 2347-2693
ER -
The data collected from MST radar uses traditional and statistical analysis for inferring wind components from the spectral data. There are several algorithms available for dimensionality reduction on big data using PCA. These algorithms are non -parametric and often implemented on high dimensional datasets. It will be quite interesting to use these analytical algorithms in the context of MST radar dataset. The existing algorithms are very week in estimation of Doppler at low SNR conditions at higher altitudes. Thus PCA algorithm has been applied on the MST Radar data to find Power Spectrum (PS) and from Power Spectrum Doppler Frequency components are estimated. The components are Zonal (U), Meridional (V), Windspeed (W) are estimated from Doppler Frequency. The PCA derived wind data has to be qualified with wind information from GPS radio-sonde thereafter.
Principal Component Analysis, MST radar, GPS sonde, Wavelet-based denoising, cepstral, thresholding
[1] V.K. Anandan, “Spectral analysis of atmospheric signal using higher orders spectral estimation technique”, IEEE Transaction, Geosci. Remote Sens. 39 (9) (Sep.2001) 1890-1895
[2] T. Sreenivasulu Reddy, “MST radar signal processing using cepstral thresholding”, IEEE Transaction Geosci. Remote Sens. 48 (6) (Jun.2010) 2704-2710
[3] Thatiparthi Sreenivasulu Reddy, “MST radar signal processing using wavelet-based denoising”, IEEE Transaction Geosci. Remote Sens. Lett. 6 (4) (Oct.2009) 752-756
[4] P. Stoica, “Smoothed non parametric spectral estimation via Cepstral thresholding”, IEEE Signal Process. Mag.23(6) (Nov.2006) 34-45
[5] D.A. Hooper, “Signal and noise level estimation for narrow spectral width returns observed by the Indian MST radar”, Radio Sci. 34(4)(1999) 859-870
