Detection in underwater noises modeled as a Gaussian-Gaussian mixture

We study statistical modeling by a Gaussian-Gaussian mixture for two different underwater noise samples. We show that one of them can be adequately described by a Gaussian-Gaussian mixture whereas the other one is very close to a Gaussian model and is described by a mixture with a very small perturbating term. The first noise is also studied with emphasis on the optimal receiver structure for the detection of a deterministic signal. The performance of two test-functions are studied. The principal result is that the use of the likelihood ratio receiver associated with the mixture model leads to improvements with respect to the classical matched filter, this improvement being measured in term of R O C curves.