GID Geoscientific Instrumentation, Methods and Data Systems Discussions GID Geosci. Instrum. Method. Data Syst. Discuss. 2193-0872 Göttingen, Germany 10.5194/gi-2017-9 An Efficient Algorithm for Improved Doppler Profile Detection of MST Radar Signals Padmaja Nimmagadda 1 Varadarajan Souri 2 Yashoda Polisetti 3 Ramyakrishna Enugonda 4 Department of Electronics and Communication Engineering, Sree Vidyanikethan Engineering College, Tirupati, Andhra Pradesh, India-517102 Department of ECE, S V University College of Engineering, Tirupati, Andhra Pradesh, India National Atmospheric Research Laboratory, Indian Space Research Organization, AP, India Sree Vidyanikethan Engineering College, Tirupati, AP, India Indian Space Research Organisation #B.19012/72/2014-II,12/11/2014 19 06 2017 2017 1 7 Copyright: © 2017 Nimmagadda Padmaja et al. 2017 This work is licensed under the Creative Commons Attribution 3.0 Unported License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/3.0/ This article is available from https://gi.copernicus.org/preprints/gi-2017-9/ The full text article is available as a PDF file from https://gi.copernicus.org/preprints/gi-2017-9/gi-2017-9.pdf

An efficient algorithm based on Empirical Mode Decomposition (EMD) de-noising using soft threshold techniques for accurate doppler profile detection and Signal to Noise Ratio (SNR) improvement of MST Radar Signals is discussed in this paper. Hilbert Huang Transform (HHT) is a time-frequency analysis technique for processing radar echoes which constitutes EMD process that decomposes the non-stationary signals into Intrinsic Mode Functions (IMFs). HHT process has been applied on the time series data of MST (Mesosphere-Stratosphere–Troposphere) radar collected from NARL (National Atmospheric research Laboratory), Gadanki, India. Further, spectral moments were estimated and signal parameters such as mean doppler, signal power, noise power and SNR were calculated. Stacked doppler profile was plotted to observe the improvement in doppler detection. It has been observed that there is a considerable improvement in recognition of the doppler echo leading to improved Signal Power and SNR. The algorithm was tested for its efficacy on various data sets for all the 6 beams and the results of two data sets are presented.