Abstract
The cyclostationarity property of GNSS signals has not been well addressed in the specialized literature so far and its potential usefulness in the context of a GNSS receiver is yet to be studied. In our paper, we build a novel analytical framework for exploiting the cyclostationarity properties of BOC-modulated GNSS signals and we illustrate how these properties may be used in the context of a multi-system multi-frequency positioning receiver in the future. We also introduce an algorithm based on the Spectral Correlation Function (SCF) for signal identification in the context of a cognitive positioning architecture and we compare it with classical Power Spectral Density (PSD) analysis. Statistical performance is studied in the presence of stationary wideband and narrowband noise sources. It is shown that distinguishing signals based on cyclostationarity properties has high potential in future cognitive positioning receivers. Copyright © 2014 Institute of Navigation.
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