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Type: Thesis
Title: Aspects of HF communications: HF noise and signal features.
Author: Giesbrecht, James E.
Issue Date: 2008
School/Discipline: School of Electrical and Electronic Engineering
Abstract: To many, high-frequency (HF) radio communications is obsolete in this age of long distance satellite communications and undersea optical fiber. Yet despite this, the HF band is used by defence agencies for backup communications and spectrum surveillance, and is monitored by spectrum management organizations to enforce licensing. Such activity usually requires systems capable of locating distant transmitters, separating valid signals from interference and noise, and recognizing signal modulation. Research presented here targets the latter issue. The ultimate aim is to develop robust algorithms for automatic modulation recognition of real HF signals, where real means signals propagating by multiple ionospheric modes with co-channel signals and non- Gaussian noise. However, many researchers adopt Gaussian noise models for signals for the sake of convenience at the cost of accuracy. Furthermore, literature describing the probability density function (PDF) of HF noise does not abound. So an additional aim of this research is measurement of the PDF of HF noise. A simple empirical technique, not found in the literature, is described that supports the hypothesis that HF noise is generally not Gaussian. In fact, the probability density function varies with the time of day, electromagnetic environment, and state of the ionosphere. Key contributions of this work relate to the statistics of HF noise and the discrimination of real HF signals via three signal features. Through two unique experiments, the density function of natural HF noise is found to closely follow a Bi-Kappa distribution. This distribution can model natural and man-made HF noise through a single control parameter. Regarding signal features, the coherence function is found to be a brute-force technique suitable only for hard (not soft) decisions. A novel application of an entropic distance measure proves able to separate four real HF signals based on their modulation types. And, an estimator for signal-to-noise (SNR) ratio is shown to provide reasonable measures of SNR for the same real HF signals.
Advisor: Abbott, Derek
Clarke, Russell
Dissertation Note: Thesis (Ph.D.) - University of Adelaide, School of Electrical and Electronic Engineering, 2008
Keywords: HF noise; Feature extraction; Bi-Kappa distribution; Coherence; Entropy; Signal-to-noise ratio; Modulation recognition; Real signals
Provenance: Copyright material removed from digital thesis. See print copy in University of Adelaide Library for full text.
Appears in Collections:Research Theses

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