Pseudo-random code vocabulary extension using dual signal correlation processing

2019 ◽  
Vol 2 (1) ◽  
pp. 100-111 ◽  
Author(s):  
V. O. Chebachev ◽  
Keyword(s):  
Random Code ◽  
Correlation Processing ◽  
Signal Correlation

Research on Computer Information Processing Technology Based on Signal Autocorrelation Domain

2014 ◽  
Vol 539 ◽  
pp. 920-924
Author(s):  
Zhan Yang Mao
Keyword(s):  
Processing System ◽  
Computer Processing ◽  
Response Curves ◽  
Basketball Players ◽  
Technology Analysis ◽  
Correlation Processing ◽  
Temperature Signal ◽  
Fluent Software ◽  
Sliding Technique ◽  
Signal Correlation

The paper establishes signal correlation processing mathematical model according to Ordinary differential equations with boundary value, which is introduced into the analysis on basketball virtual simulation sliding technique, and it designs the signal autocorrelation computer processing system. In order to verify the reliability and practicality of the system in the basketball technology analysis, this paper extracts the temperature signal using the FLUENT software, it obtains the isotherm diagram and temperature distribution cloud picture, verifies the effectiveness of the system. Finally, it verifies the practicality system in basketball technology analysis, taking analysis of back gliding speed rhythm of basketball as an example, and it gets the basketball shuffle rhythm control 3D signal response curves, which provides technical reference for basketball players.

A new method for weak quasar signal correlation and detection

2015 ◽  
Author(s):  
Tang Jifei ◽  
Meng Qiao ◽  
Lei Yezi ◽  
Xi Yuhao
Keyword(s):  
New Method ◽  
Signal Correlation

A new method used for high speed pseudo-random code generating

2014 ◽  
Author(s):  
Zhanfeng Zhao ◽  
Dong Zhang ◽  
Zhiquan Zhou
Keyword(s):  
High Speed ◽  
New Method ◽  
Random Code

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.

scholarly journals Accuracy of a Model-Free Algorithm for Temporal InSAR Tropospheric Correction

2021 ◽  
Vol 13 (3) ◽  
pp. 409
Author(s):  
Howard Zebker
Keyword(s):  
Time Series ◽  
Signal To Noise Ratio ◽  
Artifact Reduction ◽  
Interferometric Synthetic Aperture Radar ◽  
Spatial Properties ◽  
Model Free ◽  
Weather Model ◽  
Phase Variations ◽  
Source Of Error ◽  
Signal Correlation

Atmospheric propagational phase variations are the dominant source of error for InSAR (interferometric synthetic aperture radar) time series analysis, generally exceeding uncertainties from poor signal to noise ratio or signal correlation. The spatial properties of these errors have been well studied, but, to date, their temporal dependence and correction have received much less attention. Here, we present an evaluation of the magnitude of tropospheric artifacts in derived time series after compensation using an algorithm that requires only the InSAR data. The level of artifact reduction equals or exceeds that from many weather model-based methods, while avoiding the need to globally access fine-scale atmosphere parameters at all times. Our method consists of identifying all points in an InSAR stack with consistently high correlation and computing, and then removing, a fit of the phase at each of these points with respect to elevation. A comparison with GPS truth yields a reduction of three, from a rms misfit of 5–6 to ~2 cm over time. This algorithm can be readily incorporated into InSAR processing flows without the need for outside information.

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