Weak transient signal detection via a polynomial eigenvalue decomposition

Weiss, Stephan and Matthews, James and Jackson, Ben (2021) Weak transient signal detection via a polynomial eigenvalue decomposition. In: Isaac Newton Institute: The Future of Mathematical Challenges in the Electromagnetic Environment, 2021-07-27 - 2021-07-28, Isaac Newton Institute.

[thumbnail of Weiss-etal-INI-2021-Weak-transient-signal-detection-via-a-polynomial-eigenvalue]
Preview
 Text. Filename: Weiss_etal_INI_2021_Weak_transient_signal_detection_via_a_polynomial_eigenvalue.pdf
Accepted Author Manuscript
Download (498kB)| Preview

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.

ORCID iDs

Weiss, Stephan ORCID logoORCID: https://orcid.org/0000-0002-3486-7206, Matthews, James and Jackson, Ben;
CORE (COnnecting REpositories)