Abstract
The Akebono satellite was launched in 1989 to observe the Earth’s magnetosphere and plasmasphere. Omega was a navigation system with 8 ground stations transmitter and had transmission pattern that repeats every 10 s. From 1989 to 1997, the PFX on board the Akebono satellite received signals at 10.2 kHz from these stations. Huge amounts of PFX data became valuable for studying the propagation characteristics of VLF waves in the ionosphere and plasmasphere. In this study, we introduce a method for automatic detection of Omega signals from the PFX data in a systematic way, it involves identifying a transmission station, calculating the delay time, and estimating the signal intensity. We show the reliability of the automatic detection system where we able to detect the omega signal and confirmed its propagation to the opposite hemisphere along the Earth’s magnetic field lines. For more than three years (39 months), we detected 43,734 and 111,049 signals in the magnetic and electric field, respectively, and demonstrated that the proposed method is powerful enough for the statistical analyses.
Highlights
The Akebono (EXOS-D) satellite was launched at 23:30 UT on February 21, 1989 to observe the Earth’s magnetosphere and plasmasphere
This satellite has onboard very low frequency (VLF) instruments and Poynting flux Analyzer (PFX) is one of the subsystems
We present the technique we are using for automatic detection
Summary
The Akebono (EXOS-D) satellite was launched at 23:30 UT on February 21, 1989 to observe the Earth’s magnetosphere and plasmasphere. From 1989 to 1997, the PFX on board the Akebono satellite received signals at 10.2 kHz from the eight stations and huge amounts of PFX data became valuable to study the propagation characteristics of VLF waves in the ionosphere and plasmasphere. A tomographic electron density profile could be determined by calculating the Omega signal propagation path using the ray tracing method This method could estimate the propagation path within one hour of single satellite observations [2]. This study discusses the automatic detection methods for faster analysis of huge amounts of PFX data to study the propagation characteristics of VLF waves which, in this case, is the Omega signal. We present the technique we are using for automatic detection It involves identifying a transmission station, calculating the delay time, and estimating the signal intensity. We present conclusions and summarize our research
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More From: International Journal of Advanced Computer Science and Applications
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