Weak transient signal detection via a polynomial eigenvalue decomposition

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.