scholarly journals Microwave delay lines for analogue signal correlation

2020 ◽  
Author(s):  
Zenon Szczepaniak ◽  
Waldemar Susek
Keyword(s):  
Delay Lines ◽  
Analogue Signal ◽  
Signal Correlation

Editorial. Current-mode analogue signal processing

1990 ◽  
Vol 137 (2) ◽  
pp. 61 ◽  
Author(s):  
Chris Toumazou ◽  
John Lidgey ◽  
Brett Wilson
Keyword(s):  
Signal Processing ◽  
Current Mode ◽  
Analogue Signal

A Feed-Forward Time Amplifier Using a Phase Detector and Variable Delay Lines

2013 ◽  
Vol E96.C (6) ◽  
pp. 920-922 ◽  
Author(s):  
Kiichi NIITSU ◽  
Naohiro HARIGAI ◽  
Takahiro J. YAMAGUCHI ◽  
Haruo KOBAYASHI
Keyword(s):  
Phase Detector ◽  
Variable Delay ◽  
Delay Lines ◽  
Feed Forward ◽  
Time Amplifier

THE EFFECT OF LOSS-TANGENT ON LADDERING BEHAVIOR IN DELAY LINES

2009 ◽  
Vol 12 ◽  
pp. 161-170 ◽  
Author(s):  
Ali Kabiri ◽  
Mohammed M. Bait-Suwailam ◽  
Mohammad H. Kermani ◽  
Omar M. Ramahi
Keyword(s):  
Loss Tangent ◽  
Delay Lines

Digital Tapped Delay Lines for HWIL Testing of Matched Filter Radar Receivers

2009 ◽  
Author(s):  
Richard F. Olson ◽  
William J. Braselton ◽  
Richard D. Mohlere
Keyword(s):  
Matched Filter ◽  
Delay Lines ◽  
Tapped Delay Lines

Parasitic-compensated switched-capacitor delay lines

1988 ◽  
Vol 24 (7) ◽  
pp. 377 ◽  
Author(s):  
V.F. Dias ◽  
J.E. Franca
Keyword(s):  
Switched Capacitor ◽  
Delay Lines

scholarly journals NLOS Multipath Classification of GNSS Signal Correlation Output Using Machine Learning

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2503
Author(s):  
Taro Suzuki ◽  
Yoshiharu Amano
Keyword(s):  
Machine Learning ◽  
Satellite System ◽  
Training Data ◽  
Support Vector ◽  
Positioning Errors ◽  
Automated Method ◽  
Global Navigation Satellite ◽  
Better Than ◽  
Signal Correlation

This paper proposes a method for detecting non-line-of-sight (NLOS) multipath, which causes large positioning errors in a global navigation satellite system (GNSS). We use GNSS signal correlation output, which is the most primitive GNSS signal processing output, to detect NLOS multipath based on machine learning. The shape of the multi-correlator outputs is distorted due to the NLOS multipath. The features of the shape of the multi-correlator are used to discriminate the NLOS multipath. We implement two supervised learning methods, a support vector machine (SVM) and a neural network (NN), and compare their performance. In addition, we also propose an automated method of collecting training data for LOS and NLOS signals of machine learning. The evaluation of the proposed NLOS detection method in an urban environment confirmed that NN was better than SVM, and 97.7% of NLOS signals were correctly discriminated.

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