Low-Complexity Prediction of Energy Statistic Exceedance Probability for η-μ Variates

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 η - μ 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.