Signal Detection Based on a Decreasing Exponential Function in Alpha-Stable Distributed Noise
Jinjun Luo, Shilian Wang, Eryang Zhang · KSII Transactions on Internet and Information Systems · 2018
Signal detection in symmetric alpha-stable ( S Sα ) distributed noise is a challenging problem.This paper proposes a detector based on a decreasing exponential function (DEF).The DEF detector can effectively suppress the impulsive noise and achieve good performance in the presence of S S α noise.The analytical expressions of the detection and false alarm probabilities of the DEF detector are derived, and the parameter optimization for the detector is discussed.A performance analysis shows that the DEF detector has much lower computational complexity than the Gaussian kernelized energy detector (GKED), and it performs better than the latter in S S α noise with small characteristic exponent values.In addition, the DEF detector outperforms the fractional lower order moment (FLOM)-based detector in S S α noise for most characteristic exponent values with the same order of magnitude of computational complexity.