Unknown Signal Detection Using a Digital Total-Power Radiometer with Finite Internal Precision

2006 ◽  
Author(s):  
S. Koivu
Keyword(s):  
Signal Detection ◽  
Total Power ◽  
Unknown Signal

scholarly journals СРАВНИТЕЛЬНЫЙ АНАЛИЗ АЛГОРИТМОВ ОБНАРУЖЕНИЯ НЕИЗВЕСТНЫХ СИГНАЛОВ С УЧЁТОМ СОВОКУПНОСТИ ПОКАЗАТЕЛЕЙ КАЧЕСТВА

2018 ◽  
pp. 67-74
Author(s):  
Валерий Михайлович Безрук ◽  
Станислав Андреевич Иваненко
Keyword(s):  
Comparative Analysis ◽  
Signal Detection ◽  
Quality Indicators ◽  
Performance Indicators ◽  
A Priori ◽  
Pareto Optimal ◽  
Detection Algorithms ◽  
A Priori Uncertainty ◽  
Unknown Signal

The subject of this article is the problem of detecting unknown signals in conditions of high a priori uncertainty, which can occur during the determination of unoccupied frequency channels in cognitive networks. It should be noted that various sources of radio emissions work on the air, which in turn complicates the solution of the problem of detection, since it is impossible to say what kind of signal will be received. Most existing algorithms require information about the signals for their operation. In practice, it is not possible to have such data on all sources of radio emission due to their diversity. The goal of the article is to study non-classical signal detection algorithms in conditions of high a priori uncertainty, when there is information only about noise, and signals are unknown.  The task: to conduct a comparative analysis of unknown signal detection algorithms based on a set of quality indicators and to determine the set of Pareto-optimal detection algorithms, as well as to identify the best algorithm for a set of quality indicators.  The method of statistical modeling of detection algorithms on samples of real signals and noise is performed. As a result, we obtained estimates of speed of work and quality of signal detection for a number of different variants of unknown signal detection algorithms. Possible variants of implementation of the detectors were summarized in the table. These variants were formed taking into account the dimension of the DPF sample and the number of implementations on which the decision is made. A comparative analysis of different types of detection algorithms is carried out taking into account the set of performance indicators and the quality of signal detection. It should be noted that the values of quality indicators of detection of unknown signals and performance indicators of the algorithms are related and contradictory. Conclusions. A multicriteria selection of a subset of Pareto-optimal variants is performed. Using the conditional preference criterion, the only preferred variant of the algorithm for detecting unknown signals is selected from the Pareto subset. The results of the research can be used in automated radio monitoring in cognitive radio networks

scholarly journals Unknown signal detection by one-class detector based on Gaussian copula

2014 ◽  
Vol 96 ◽  
pp. 315-320 ◽  
Author(s):  
Antonio Soriano ◽  
Luis Vergara ◽  
Jorge Moragues ◽  
Ramón Miralles
Keyword(s):  
Signal Detection ◽  
Gaussian Copula ◽  
Unknown Signal

Unknown Signal Detection in Interference and Noise Using Hidden Markov Models

2021 ◽  
Author(s):  
Gabriel Ford ◽  
Benjamin J. Foster ◽  
Michael J. Liston ◽  
Moshe Kam
Keyword(s):  
Signal Detection ◽  
Hidden Markov Models ◽  
Markov Models ◽  
Hidden Markov ◽  
Unknown Signal

Sample Size and the Detection of Correlation--A Signal Detection Account: Comment on Kareev (2000) and Juslin and Olsson (2005).

2005 ◽  
Vol 112 (1) ◽  
pp. 268-279 ◽  
Author(s):  
Richard B. Anderson ◽  
Michael E. Doherty ◽  
Neil D. Berg ◽  
Jeff C. Friedrich
Keyword(s):  
Signal Detection ◽  
Sample Size
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