TY - CONF T1 - Locally optimal invariant detector for testing equality of two power spectral densities T2 - IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) Y1 - 2018 A1 - David Ramírez A1 - Daniel Romero A1 - Javier Vía A1 - R. López-Valcarce A1 - Ignacio Santamaría KW - cognitive radio KW - winter AB -
This work addresses the problem of determining whether two multivariate random time series have the same power spectral density
(PSD), which has applications, for instance, in physical-layer security and cognitive radio. Remarkably, existing detectors for this
problem do not usually provide any kind of optimality. Thus, we study here the existence under the Gaussian assumption of optimal
invariant detectors for this problem, proving that the uniformly most powerful invariant test (UMPIT) does not exist. Thus, focusing on
close hypotheses, we show that the locally most powerful invariant test (LMPIT) only exists for univariate time series. In the multivariate
case, we prove that the LMPIT does not exist. However, this proof suggests two LMPIT-inspired detectors, one of which outperforms
previously proposed approaches, as computer simulations show.
JF - IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) CY - Calgary, Canada ER -