High-frequency (HF) communications is undergoing resurgence despite advances in long-range satellite communication systems. Defense agencies are using the HF spectrum for backup communications as well as for spectrum surveillance applications. Spectrum management organizations are monitoring the HF spectrum to control and enforce licensing. These activities usually require systems capable of determining the location of a source of transmissions, separating valid signals from interference and noise, and recognizing signal modulation. Our ultimate aim is to develop robust modulation recognition algorithms for real HF signals, that is, signals propagating by multiple ionospheric modes.One aspect of modulation recognition is the extraction of signal identifying features. The most common features for modulation recognition are instantaneous phase, amplitude, and frequency. However, this paper focuses on two feature parameters: coherence and entropy. Signal entropy and the coherence function show potential for robust recognition of HF modulation types in the presence of HF noise and multi-path. Specifically, it is shown that the methods of calculation of coherence and entropy are important and that appropriate calculations ensure stability in the parameters. For the first time a new metric, called Coherence-Median Difference (CMD), is introduced that provides a measure of the dominance of coherence at specific frequencies to coherence at all other frequencies in a particular bandwidth.

To many, high-frequency (HF) radio communications is obsolete in this age of longdistance satellite communications and undersea optical fiber. Yet despite this, the HF band is used by defense 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. Our research targets the latter issue. The ultimate aim is to develop robust algorithms for automatic modulation recognition of real HF signals. By real, we mean signals propagating by multiple ionospheric modes with co-channel signals and non-Gaussian noise. However, many researchers adopt Gaussian noise for their modulation recognition algorithms 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 we describe a simple empirical technique, not found in the literature, that supports our work by showing that the probability density function (PDF) for HF noise is generally not Gaussian. In fact, the probability density function varies with the time of day, electro-magnetic environment, and state of the ionosphere.

High-frequency (HF) communications is undergoing resurgence despite advances in long-range satellite communication systems. Defense agencies are using the HF spectrum for backup communications, as well as for spectrum surveillance applications. Spectrum management organizations are monitoring the HF spectrum to control and enforce licensing. These activities usually require systems capable of determining the location of a source of transmissions, separating valid signals from interference and noise, and recognizing signal modulation. Our ultimate aim is to develop robust modulation recognition algorithms for real HF signals, that is, signals propagating by multiple ionospheric modes with cochannel signals and non-Gaussian noise.One aspect of modulation recognition is the extraction of signal identifying features. This paper continues our work of applying various feature parameters to real HF signals and gives guidance on which features show potential for use in robust recognition of HF modulation types in the presence of HF noise and multi-path. It also defines a measure of mean separation distance between modulation types based on an entropy parameter, and discusses the probability density function of HF noise.

This paper reviews modulation recognition in the context of HF radio-communications. We investigate entropic distance measures and coherence measures for recognizing HF modulations. Preliminary results shown that it may be possible to identify a modulation and its transmit power level based on the entropic distance between it and another modulation. Coherence estimates may provide characteristic signatures that can be used to identify modulation types.

High-frequency (HF) communications is undergoing a resurgence despite advances in long-range satellite communication systems. Defense agencies are using the HF spectrum for backup communications as well as for spectrum surveillance applications. Spectrum management organizations are monitoring the HF spectrum to control and enforce licensing. These activities usually require systems capable of determining the location of a source of transmissions, separating valid signals from interference and noise, and recognizing signal modulation. Our ultimate aim is to develop robust modulation recognition algorithms for real HF signals that propagate by multiple ionospheric modes.One aspect of modulation recognition is the extraction of signal identifying features. The most common features for modulation recognition are instantaneous phase, amplitude, and frequency. Many papers present results based on synthetic data and unproven assumptions. However, this paper continues our previous work by applying the coherence function to noisy real HF groundwave signals; which removes the need for synthesized data and unrealistic assumptions.