Signal Detection in Correlated Non-Gaussian Noise Using Higher-Order Statistics

The authors of this paper study the synthesis of new models and methods for signal detection in additive correlated non-Gaussian noise. A new moment quality criterion decision making is proposed based on a random process description using moments and a formation of polynomial decision rules. Taking...

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Bibliographic Details
Published in:Circuits, systems, and signal processing systems, and signal processing, 2018-04, Vol.37 (4), p.1704-1723
Main Authors: Palahina, Elena, Gamcová, Mária, Gladišová, Iveta, Gamec, Ján, Palahin, Volodymyr
Format: Article
Language:English
Subjects:
Circuits and Systems
Data processing
Decision making
Electrical Engineering
Electronics and Microelectronics
Engineering
Gaussian distribution
Gaussian process
Instrumentation
Noise
Normal distribution
Random noise
Random variables
Signal detection
Signal processing
Signal,Image and Speech Processing
Statistical analysis
Statistical methods
Synthesis
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Summary:The authors of this paper study the synthesis of new models and methods for signal detection in additive correlated non-Gaussian noise. A new moment quality criterion decision making is proposed based on a random process description using moments and a formation of polynomial decision rules. Taking into account parameters of non-Gaussian distribution of random variables (such as the moments of third and higher orders and joint cumulants), it is shown that nonlinear processing of samples can increase the signal processing efficiency. A synthesis of effective methods and algorithms of data processing in non-Gaussian noise is also presented in this work.
ISSN:0278-081X
1531-5878
DOI:10.1007/s00034-017-0623-5