Unknown Signal Detection in Interference and Noise Using Hidden Markov Models

Gabriel Ford, Benjamin J. Foster, Michael J. Liston, Moshe Kam

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

A machine learning approach to detecting unknown or anomalous signals in a complicated background of interfering signals and noise is presented. A hidden Markov model (HMM) is trained to represent the interference and noise environment via a Bayesian nonparametric hierarchical Dirichlet process (HDP)-HMM technique. An unknown signal is detected if the Viterbi hidden state path of the test data is sufficiently unlikely under the learned background HMM. The detection scheme is derived as a generalized likelihood ratio test (GLRT) for an unknown deterministic signal in HMM noise. In simple additive white Gaussian noise (AWGN), the proposed scheme trivially reduces to an energy detector. However, experimental results on a software-defined radio (SDR) testbed demonstrate that the proposed scheme substantially outperforms energy detection in a more challenging interference and noise background. The approach can be employed in spectrum monitoring applications to efficiently detect transmissions that deviate from a typical signal environment.

Original languageEnglish (US)
Title of host publication2021 IEEE Statistical Signal Processing Workshop, SSP 2021
PublisherIEEE Computer Society
Pages406-410
Number of pages5
ISBN (Electronic)9781728157672
DOIs
StatePublished - Jul 11 2021
Event21st IEEE Statistical Signal Processing Workshop, SSP 2021 - Virtual, Rio de Janeiro, Brazil
Duration: Jul 11 2021Jul 14 2021

Publication series

NameIEEE Workshop on Statistical Signal Processing Proceedings
Volume2021-July

Conference

Conference21st IEEE Statistical Signal Processing Workshop, SSP 2021
Country/TerritoryBrazil
CityVirtual, Rio de Janeiro
Period7/11/217/14/21

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering
  • Applied Mathematics
  • Signal Processing
  • Computer Science Applications

Keywords

  • Bayesian nonparametrics
  • generalized likelihood ratio test
  • hidden Markov model
  • Signal detection

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