Statistical Analysis and Modeling of Low-Frequency Radio Noise and Improvement of Low-Frequency Communications

Abstract

Abstract : Naturally occurring radio noise above approximately 100 MHz is well modeled for most applications as a Gaussian random process; however, atmospheric radio noise below 100 MHz is often impulsive in nature and is not well modeled as Gaussian. Atmospheric events (mainly lightning strokes, which create electromagnetic emissions known as sferics) produce large, clustered impulses in the noise waveform seen at a receiving antenna, causing the waveform to vary greatly from typical Gaussian background noise. Due to large variations in sferic activity on a seasonal and diurnal basis and with the passing of individual storms, atmospheric noise is also non-stationary. The objective of this work is the statistical characterization and modeling of atmospheric radio noise in the range 10 Hz - 60 kHz (denoted low-frequency noise), with the specific goal of improving communication systems operating in this range. The analyses are based on many thousands of hours of measurements made by the Stanford Radio Noise Survey System.