Unknown Signal Detection in Switching Linear Dynamical System Noise

A machine learning approach is presented for detecting unknown or anomalous signals in a complicated background of interfering signals and noise. The approach can be employed in RF spectrum monitoring applications to efficiently detect transmissions that deviate from a typical signal environment. Fo...

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Veröffentlicht in:	IEEE transactions on signal processing 2023-01, Vol.71, p.1-14
Hauptverfasser:	Ford, Gabriel, Foster, Benjamin J., Braun, Stephen A., Kam, Moshe
Format:	Artikel
Sprache:	eng
Schlagworte:	Autoregressive processes Background noise Bayesian nonparametrics Cognitive radio Detectors Dirichlet problem Dynamical systems Empirical analysis False alarms generalized likelihood ratio test Hidden Markov models Interference Likelihood ratio Machine learning Markov jump linear system Signal detection Software radio Statistical analysis Superluminescent diodes Switches Switching switching linear dynamical system Test stands Upper bounds Viterbi algorithm
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