Robust RF Mixture Signal Recognition Using Discriminative Dictionary Learning

RF signal recognition is an important element toward RF situational awareness and dynamic spectrum management. In this work, machine learning-based signal recognition algorithms are proposed. Our key contribution is to engineer feature learning such that the classifiers can perform robustly even when a mixture of heterogeneous signal classes is observed, although the training is still done using non-mixture single-label samples. To achieve this, discriminative dictionary learning algorithms are developed with various feature-shaping constraints. The signal detection can then be done in a way reminiscent of the multi-user detection in wireless communication, employing linear equalizers. The algorithms are tested using real wideband RF measurement data. It is verified that the proposed algorithms can robustly classify the component signals even when their powers are widely different and their number is not known a priori.

Automated summary

Machine learning-based signal recognition algorithms are proposed in this work, utilizing discriminative dictionary learning algorithms with various feature-shaping constraints. The classifiers can robustly classify component signals even when a mixture of heterogeneous signal classes is observed, similar to multi-user detection in wireless communication. The algorithms are tested using real wideband RF measurement data, demonstrating their ability to classify signals effectively.