TY - GEN

T1 - Detection of Gaussian signals in unknown time-varying channels

AU - Romero, Daniel

AU - Vía, Javier

AU - López-Valcarce, Roberto

AU - Santamaría, Ignacio

PY - 2012/11/6

Y1 - 2012/11/6

N2 - Detecting the presence of a white Gaussian signal distorted by a noisy time-varying channel is addressed by means of three different detectors. First, the generalized likelihood ratio test (GLRT) is found for the case where the channel has no temporal structure, resulting in the well-known Bartlett's test. Then it is shown that, under the transformation group given by scaling factors, a locally most powerful invariant test (LMPIT) does not exist. Two alternative approaches are explored in the low signal-to-noise ratio (SNR) regime: the first assigns a prior probability density function (pdf) to the channel (hence modeled as random), whereas the second assumes an underlying basis expansion model (BEM) for the (now deterministic) channel and obtains the maximum likelihood (ML) estimates of the parameters relevant for the detection problem. The performance of these detectors is evaluated via Monte Carlo simulation.

AB - Detecting the presence of a white Gaussian signal distorted by a noisy time-varying channel is addressed by means of three different detectors. First, the generalized likelihood ratio test (GLRT) is found for the case where the channel has no temporal structure, resulting in the well-known Bartlett's test. Then it is shown that, under the transformation group given by scaling factors, a locally most powerful invariant test (LMPIT) does not exist. Two alternative approaches are explored in the low signal-to-noise ratio (SNR) regime: the first assigns a prior probability density function (pdf) to the channel (hence modeled as random), whereas the second assumes an underlying basis expansion model (BEM) for the (now deterministic) channel and obtains the maximum likelihood (ML) estimates of the parameters relevant for the detection problem. The performance of these detectors is evaluated via Monte Carlo simulation.

KW - Detection theory

KW - basis expansion model

KW - generalized likelihood ratio

KW - locally most powerful invariant

KW - time-varying channels

UR - http://www.scopus.com/inward/record.url?scp=84868236130&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84868236130&partnerID=8YFLogxK

U2 - 10.1109/SSP.2012.6319858

DO - 10.1109/SSP.2012.6319858

M3 - Conference contribution

AN - SCOPUS:84868236130

SN - 9781467301831

T3 - 2012 IEEE Statistical Signal Processing Workshop, SSP 2012

SP - 916

EP - 919

BT - 2012 IEEE Statistical Signal Processing Workshop, SSP 2012

T2 - 2012 IEEE Statistical Signal Processing Workshop, SSP 2012

Y2 - 5 August 2012 through 8 August 2012

ER -