Low-Complexity Prediction of Energy Statistic Exceedance Probability for $\eta$-$\mu$ Variates
Year: 2024
Type: article
Abstract: Characterization of the exceedance probability (EP) of the energy statistic (ES) plays a fundamental role in several signal processing applications, including radar (e.g., probability of false alarm) and communications (e.g., outage probability). However, manageable closed-form expressions are not available for general non-Gaussian models such as the$\eta$-$\mu$distribution. In this letter, simple formulas for predicting the EP of the ES are provided, based on second- and third-order cumulant series expansion of the tightest Chernoff bound, coupled with low-complexity approximations of Hoyt moments. Results show that the proposed method significantly improves over earlier work based on different bounds, and outperforms the asymptotic approximation via the central limit theorem as well as the Generalized Pareto Distribution fitting of the distribution tail.
Source: IEEE Signal Processing Letters
Authors: Angelo Coluccia, Alessio Fascista
Institutions: University of Salento, Polytechnic University of Bari
Language: English
Subfield: Aerospace Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $2,495
OpenAlex ID: W4404035082