Open AccessDesign and performance analysis of parametric suboptimal detectors in S α S noise
Author(s) -
Dai Zhen,
Wang Pingbo,
Cao Weihao
Publication year - 2020
Publication title -
iet communications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.355
H-Index - 62
eISSN - 1751-8636
pISSN - 1751-8628
DOI - 10.1049/iet-com.2019.0606
Subject(s) - detector , parametric statistics , gaussian , gaussian noise , probability density function , algorithm , detection theory , computer science , noise (video) , cauchy distribution , mathematics , control theory (sociology) , statistics , physics , telecommunications , artificial intelligence , control (management) , quantum mechanics , image (mathematics)
The design and performance analysis of detectors for weak signals in the symmetric α‐ stable ( S α S ) noise is considered. The locally optimum detector (LOD) has the optimal performance, but in the case of S α S noise, it is hard to implement in practise since there exists no closed form for the probability density function (PDF) of the noise. To solve this problem, many parametric suboptimal detectors have been proposed such as the soft limiter, locally suboptimum detector and generalised Cauchy detector. In this study, the authors propose a new low‐complexity parametric suboptimal detector, generalised Gaussian detector, which does not need the explicit expression of the noise PDF. In addition, the maximum deflection coefficient criterion is proposed to optimise all the parametric detectors. Furthermore, they compare the performance of the suboptimal detectors with the LOD based on the maximum deflection efficacy. Then, an optimal detection programme for weak signals detection in practise is obtained, and finally simulation results provide its superiority.