Implementation of storm‐time ionospheric forecasting algorithm using SSA–ANN model. Issue 8 (30th June 2020)

• Record Type: Journal Article
• Authors: Kumar Dabbakuti, J. R. K.; Peesapati, Rangababu; Yarrakula, Mallika; Anumandla, Kiran Kumar; Madduri, Sasi Vardhan
• Is Part Of: IET radar, sonar & navigation. Volume 14:Issue 8(2020)
• Page Start: 1249
• Page End: 1255
• Publication Date: 2020-06-30
• Subjects: satellite navigation -- principal component analysis -- ionosphere -- ionospheric disturbances -- neural nets -- total electron content (atmosphere) -- magnetic storms -- Global Positioning System; SSA–ANN model -- standard principal component analysis–ANN model -- storm‐time ionospheric forecasting algorithm -- storm‐time conditions -- accurate position estimation -- storm‐time ionospheric model -- artificial neural network -- singular spectrum analysis -- pre‐processing tool -- Global Navigation Satellite System stations -- GNSS stations -- storm criterion -- storm day data sets -- model development -- GPS–TEC data sets -- geomagnetic storm days -- moderate storm; Signal processing -- Periodicals; Radar -- Periodicals; Sonar -- Periodicals; Electronics in navigation -- Periodicals; Navigation -- Periodicals; 621.3848
• DOI: 10.1049/iet-rsn.2019.0551
• Languages: English
• ISSNs: 1751-8784

Abstract

Abstract : Forecasting of total electron content (TEC)/global positioning system (GPS) signal delays in storm conditions is considered the most challenging task for accurate position estimation, especially in critical applications. Therefore, a storm‐time ionospheric model is proposed to forecast TEC based on the artificial neural network (ANN) using singular spectrum analysis (SSA). The study area covers four Global Navigation Satellite System (GNSS) stations located in the low‐latitude and two GNSS stations located in the mid‐latitude ionosphere. The geographical area extends between 11–43°N latitude and 77–93°E longitude. The selection of GPS–TEC data is based on the storm criterion of Dst ≤ −50 nT, and the storm day data sets from 2009 to 2017 period are used for model development. The proposed algorithm is tested with the GPS–TEC data sets of three geomagnetic storm days (i) severe, (ii) moderate storm and (iii) strong at low and mid‐latitudes. The average precision and mean absolute error of the proposed SSA‐ANN model is 1.41 and 1.06 TECU (strong storm), respectively. The prediction performance of the proposed SSA‐ANN model is compared with the standard principal component analysis‐ANN model. The improvement factor of the SSA‐ANN is improved by 43.82%.