Application to locally optimum detection of a new noise model

The authors discuss the need to provide a realistic model of a generic noise probability density function (PDF), in order to optimize the signal detection in non-Gaussian environments. The target is to obtain a model depending on a few parameters (that are quick and easy to estimate), and is so general that it is able to describe many kinds of noise (e.g., symmetric or asymmetric, with variable sharpness). To this end, a new HOS-based model is introduced, which is derived from the generalized Gaussian function, and depends on three parameters: kurtosis, for representing variable sharpness, and left and right variances (whose combination provides the same information of skewness) for describing the deviation from symmetry. This model is applied to the design of a locally optimum detection (LOD) test. Promising experimental results are presented which are derived from the application of the test to detecting signals corrupted by real underwater acoustic noise