Application to locally optimum detection of a new noise model

The work is addressed to provide realistic modelling of a generic noise probability density function (pdf), in order to optimize signal detection in non-Gaussian environments. The target is to obtain a model depending on few parameters (quick and easy to estimate), and so general to be 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 derives 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 deviation from symmetry. This model is applied in the design of a Locally Optimum Detection (LOD) test. Promising experimental results are presented which derive from the application of the test for detecting signals corrupted by real underwater acoustic noise.