GPS, Galileo and Glonass L1 signal detection algorithms based on bandpass sampling techniques

2012 ◽  
Author(s):  
Maher Al-Aboodi ◽  
Ali Albu-Rghaif ◽  
Ihsan Alshahib Lami
Keyword(s):  
Signal Detection ◽  
Sampling Techniques ◽  
Bandpass Sampling ◽  
Detection Algorithms

Study of BLAST Signal Detection Algorithm in LTE System

2013 ◽  
Vol 756-759 ◽  
pp. 3183-3188
Author(s):  
Tao Lei ◽  
Deng Ping He ◽  
Fang Tang Chen
Keyword(s):  
Signal Detection ◽  
High Speed ◽  
Data Communication ◽  
Detection Algorithm ◽  
Detection Performance ◽  
Detection Algorithms ◽  
High Speed Data ◽  
Simulation Results ◽  
Better Than

BLAST can achieve high speed data communication. Its signal detection directly affects performance of BLAST receiver. This paper introduced several signal detection algorithmsZF algorithm, MMSE algorithm, ZF-SIC algorithm and MMSE-SIC algorithm. The simulation results show that the traditional ZF algorithm has the worst performance, the traditional MMSE algorithm and the ZF-SIC algorithm is similar, but with the increase of the SNR, the performance of ZF-SIC algorithm is better than MMSE algorithm. MMSE-SIC algorithm has the best detection performance in these detection algorithms.

scholarly journals Drug Safety Monitoring in Children: Performance of Signal Detection Algorithms and Impact of Age Stratification

Drug Safety ◽  
2016 ◽  
Vol 39 (9) ◽  
pp. 873-881 ◽  
Author(s):  
Osemeke U. Osokogu ◽  
Caitlin Dodd ◽  
Alexandra Pacurariu ◽  
Florentia Kaguelidou ◽  
Daniel Weibel ◽  
...  
Keyword(s):  
Signal Detection ◽  
Drug Safety ◽  
Safety Monitoring ◽  
Detection Algorithms ◽  
Drug Safety Monitoring ◽  
Age Stratification

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

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