Modulation recognition for HF signals

Abstract

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.

Modulation recognition for real HF signals

“…For an 8-bit quantizer the alphabet is quite coarse compared to the 16-bit quantizer. Note that the entropic distance briefly exceeds Shannon's entropy 5 for the case where the quantizer has 16-bits. It is likely that relatively few samples at the point of overshoot causes the entropy measure to be more sensitive to the noise signal B.…”

“…Previous investigations 4,5 show that a change in received signal strength can be detected as a change in entropic distance and that it may be possible to identify a signal modulation with entropic distance.…”

“…However, this shall be deferred to later in the discussion for reasons that will soon become clear. The only review now necessary is that of previous work [3][4][5][6] on coherence and Benedetto's entropy 7 measure. Coherence is unlikely to yield good separation between signal types, but Benedetto's entropy parameter is a feasible indicator of signal type.…”

“…This paper continues the work presented earlier by Giesbrecht, Clarke, and Abbott. [3][4][5][6] In the context of the previous work, it briefly reviews the performance of the coherence function and discusses the outcome of applying Benedetto's entropy 7 measure. This paper also discusses the non-Gaussian nature of electro-magnetic noise in the HF spectrum, and describes a way forward in the development of a robust modulation recognition system.…”

Coherence as a feature of real HF signals

“…In the previous work [2][3][4] we discussed the fundamentals of coherence and entropy as well as the preliminary application of coherence 5 and entropy 6 to real HF signals. This work looks more closely at the feature of coherence for real HF signals and how it might be used in modulation recognition.…”