Stochastic signal detection based on changeable fractional lower order moments in alpha stable noise

Abstract

Non-Gaussian noises usually fail many conventional and effective signal detection techniques including the energy detector and the eigenvalue-based detector. The fractional lower order moment (FLOM) based detector has proved to be useful for unknown stochastic signal detection in α-stable distributed noises. However, the fixed exponent prevents the improvement of its performance. This paper presents a novel signal detection method based on changeable fractional lower order moments in non-Gaussian noise modeled by the α-stable distribution. The proposed detector would require the estimation of the characteristic exponent (α) and the dispersion (γ) of the background noises, to decide a proper bound using an empirical formula for piecewise processing. Computer simulations and field experiments are conducted to obtain the detection probabilities and ROC curves of the proposed detector, against the FLOM detector and Cauchy detector, in terms of the generalised signal-to-noise ratio and the characteristic exponent (α). Results show that the changeable fractional lower order moment detector significantly outperforms the FLOM based detector and Cauchy detector for small values of α and the simple implementation makes it an attractive solution for signal detection in α-stable distributed noises.