HOS-based generalized noise pdf models for signal detection optimization

Abstract This paper aims to provide a realistic modeling of a generic noise probability 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 asymmetric, with variable sharpness). To this end, three HOS-based models are proposed. They present different levels of generality, which increase with the number of HOS parameters used in the model. The most general and effective of these parameters are used in the asymmetric generalized Gaussian, which is expressed in terms of kurtosis , providing variable sharpness (from flat to impulsive shapes), and left and right variances (whose combination provides the same information as skewness ), describing eventual deviation from symmetry. HOS-based pdf modelling is applied to optimize signal detection in non-Gaussian environments . The proposed models are used and compared in the design of locally optimum detection (LOD) tests. Promising experimental results are presented, being obtained by the application of the tests for detecting known signals corrupted by real underwater acoustic noise.

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