Weak transient signal detection via a polynomial eigenvalue decomposition

Stephan Weiss, James Matthews, Ben Jackson

Research output: Contribution to conferencePoster

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Abstract

We have proposed a broadband subspace approach to detect the presence of
weak transient signals; this is based on second order statistics of sensor array data — the space-time covariance matrix — and a polynomial matrix EVD; this covariance matrix and its decomposition can be computed off-line; a subspace decomposition for the noise-only subspace determines a syndrome vector; in the absence of a transient signal, this syndrome only contains noise; a transient signal is likely to protrude into the noise-only subspace, and a change in
energy can be detected even if the signal is weak; discrimination can be traded off against decision time; further work: (i) impact of time-varying channels, and (ii) forensic investigation of the transient source once detected.
Original languageEnglish
Number of pages19
Publication statusPublished - 27 Jul 2021
EventIsaac Newton Institute: The Future of Mathematical Challenges in the Electromagnetic Environment - Isaac Newton Institute, Cambridge, United Kingdom
Duration: 27 Jul 202128 Jul 2021
https://gateway.newton.ac.uk/event/tgmw99/programme

Workshop

WorkshopIsaac Newton Institute: The Future of Mathematical Challenges in the Electromagnetic Environment
Abbreviated titleCEME
Country/TerritoryUnited Kingdom
CityCambridge
Period27/07/2128/07/21
Internet address

Keywords

  • transient signal detection
  • eigenvalue decomposition
  • polynomial matrix

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