HOS-BASED GENERALIZED NOISE PDF MODELS FOR SIGNAL-DETECTION OPTIMIZATION

This paper aims to provide a realistic modeling of a generic noise pro bability density function (pdf). The target is to obtain a parametric model dependent on few parameters (simple to estimate), and so general to be able to describe many kinds of noise (e.g., symmetric or asymme tric, with variable sharpness). To this end, three HOS-based models ar e proposed. They present different levels of generality, which increas e with the number of HOS parameters used in the model. The most genera l and effective of these parameters are used in the asymmetric general ized Gaussian, which is expressed in terms of kurtosis, providing vari able sharpness (from flat to impulsive shapes), and left and right var iances (whose combination provides the same information as skewness), describing eventual deviation from symmetry. HOS-based pdf modelling i s applied to optimize signal detection in non-Gaussian environments. T he proposed models are used and compared in the design of locally opti mum detection (LOD) tests. Promising experimental results are presente d, being obtained by the application of the tests for detecting known signals corrupted by real underwater acoustic noise. (C) 1998 Elsevier Science B.V. All rights reserved.