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http://dx.doi.org/10.3837/tiis.2018.01.013

Signal Detection Based on a Decreasing Exponential Function in Alpha-Stable Distributed Noise  

Luo, Jinjun (School of Electronic Science and Engineering, National University of Defense Technology)
Wang, Shilian (School of Electronic Science and Engineering, National University of Defense Technology)
Zhang, Eryang (School of Electronic Science and Engineering, National University of Defense Technology)
Publication Information
KSII Transactions on Internet and Information Systems (TIIS) / v.12, no.1, 2018 , pp. 269-286 More about this Journal
Abstract
Signal detection in symmetric alpha-stable ($S{\alpha}S$) 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 $S{\alpha}S$ 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 $S{\alpha}S$ noise with small characteristic exponent values. In addition, the DEF detector outperforms the fractional lower order moment (FLOM)-based detector in $S{\alpha}S$ noise for most characteristic exponent values with the same order of magnitude of computational complexity.
Keywords
Alpha-stable distribution; impulsive noise; decreasing exponential function; signal detection;
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