Monitoring the HF spectrum in the presence of noise

Giesbrecht, James E.; Clarke, Russell; Abbott, Derek

doi:10.1117/12.546672

Cited by 7 publications

(10 citation statements)

References 7 publications

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“…Thus coherence is indeed a "brute-force" method, as proposed previously, 3 since it is only useful for determining whether or not a signal is the same type as a reference signal. For example, if the reference signal is a BFSK signal with f m = f 1 and f s = f 2 and the received signal is a BFSK signal with f m = f 3 and f s = f 4 the coherence will only indicate that the two signals are not the same.…”

Section: Coherence Vs Snrmentioning

confidence: 99%

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Modulation recognition for HF signals

Giesbrecht

Clarke²,

Abbott

2005

Smart Structures, Devices, and Systems II

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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.

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Section: Coherence Vs Snrmentioning

confidence: 99%

“…This paper continues the work presented earlier by Giesbrecht, Clarke, and Abbott. 2,3 In the previous work we discussed the preliminary application of coherence 4 and entropy 5 to real HF signals. This work discusses in more detail the fundamentals of coherence and entropy and how they might be used in modulation recognition.…”

Section: Introductionmentioning

confidence: 99%

Modulation recognition for HF signals

Giesbrecht

Clarke²,

Abbott

2005

Smart Structures, Devices, and Systems II

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“…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.…”

Section: Entropymentioning

confidence: 99%

“…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.…”

Section: Review Of Researchmentioning

confidence: 99%

“…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.…”

Section: Introductionmentioning

confidence: 99%

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Modulation recognition for real HF signals

Giesbrecht

Clarke²,

Abbott

2005

Microelectronics: Design, Technology, and Packaging II

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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.

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Parameters for automatic modulation recognition of HF signals

Giesbrecht¹,

Clarke²,

Abbott³

2006

10th IET International Conference on Ionospheric Radio Systems and Techniques (IRST 2006)

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Two fundamental processes of modulation recognition are parameter extraction and feature selection. Parameter extraction determines unique characteristics of the signal such as frequency, phase, and amplitude. Parameters may also include statistical measures such as standard deviation, and nth-order moments and cumulants. This paper provides a brief review of two parameters, coherence and entropic distance, and reintroduces a statistical measure of signal-to-noise ratio (SNR). It also discusses the usefulness of these parameters in automatic modulation recognition ofHF signals.

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Monitoring the HF spectrum in the presence of noise

Cited by 7 publications

References 7 publications

Modulation recognition for HF signals

Modulation recognition for HF signals

Modulation recognition for real HF signals

Parameters for automatic modulation recognition of HF signals

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