Field test results of the three-dimensional acquisition and tracking (3DATA) sensor system

2004 ◽  
Author(s):  
Paul W. Fairchild ◽  
David Lilly ◽  
William B. Matkin
Keyword(s):  
Field Test ◽  
Three Dimensional ◽  
Sensor System ◽  
Test Results

Three-dimensional super-resolution: theory, modeling, and field test results

2014 ◽  
Vol 53 (12) ◽  
pp. 2583 ◽  
Author(s):  
Alexander Bulyshev ◽  
Farzin Amzajerdian ◽  
Vincent E. Roback ◽  
Glenn Hines ◽  
Diego Pierrottet ◽  
...  
Keyword(s):  
Field Test ◽  
Three Dimensional ◽  
Super Resolution ◽  
Test Results

A Robust and Efficient 3D LADAR Odometer with Outlier Detection

2017 ◽  
Vol 71 (2) ◽  
pp. 317-338
Author(s):  
Zhen Zhu ◽  
Maarten Uijt de Haag
Keyword(s):  
Outlier Detection ◽  
Field Test ◽  
Indoor Environment ◽  
Detection Method ◽  
Three Dimensional ◽  
Two Dimensional ◽  
Test Results ◽  
Estimation Algorithms ◽  
Laser Scanners ◽  
Autonomous Aircraft

This paper discusses the estimation algorithms for Three-Dimensional (3D) displacement and 3D rotation using Two-Dimensional (2D) laser scanners. An efficient outlier detection method is proposed for both algorithms to help protect the integrity of navigation. The algorithms have been evaluated using both simulation and field test results. They are able to produce a robust odometry solution for an autonomous aircraft in an indoor environment.

Long Term Field Test Results of Experimental EPDM and PUF Roofing

1990 ◽  
Author(s):  
David M. Bailey ◽  
Stuart D. Foltz ◽  
Myer J. Rosenfield
Keyword(s):  
Field Test ◽  
Test Results ◽  
Term Field

scholarly journals Precise point positioning with GPS and Galileo broadcast ephemerides

GPS Solutions ◽  
2021 ◽  
Vol 25 (2) ◽  
Author(s):  
Luca Carlin ◽  
André Hauschild ◽  
Oliver Montenbruck
Keyword(s):  
Precise Point Positioning ◽  
Three Dimensional ◽  
Test Results ◽  
Process Noise ◽  
Functional Models ◽  
Ambiguity Estimation ◽  
Point Positioning ◽  
And Performance ◽  
Range Error ◽  
Space Range

AbstractFor more than 20 years, precise point positioning (PPP) has been a well-established technique for carrier phase-based navigation. Traditionally, it relies on precise orbit and clock products to achieve accuracies in the order of centimeters. With the modernization of legacy GNSS constellations and the introduction of new systems such as Galileo, a continued reduction in the signal-in-space range error (SISRE) can be observed. Supported by this fact, we analyze the feasibility and performance of PPP with broadcast ephemerides and observations of Galileo and GPS. Two different functional models for compensation of SISREs are assessed: process noise in the ambiguity states and the explicit estimation of a SISRE state for each channel. Tests performed with permanent reference stations show that the position can be estimated in kinematic conditions with an average three-dimensional (3D) root mean square (RMS) error of 29 cm for Galileo and 63 cm for GPS. Dual-constellation solutions can further improve the accuracy to 25 cm. Compared to standard algorithms without SISRE compensation, the proposed PPP approaches offer a 40% performance improvement for Galileo and 70% for GPS when working with broadcast ephemerides. An additional test with observations taken on a boat ride yielded 3D RMS accuracy of 39 cm for Galileo, 41 cm for GPS, and 27 cm for dual-constellation processing compared to a real-time kinematic reference solution. Compared to the use of process noise in the phase ambiguity estimation, the explicit estimation of SISRE states yields a slightly improved robustness and accuracy at the expense of increased algorithmic complexity. Overall, the test results demonstrate that the application of broadcast ephemerides in a PPP model is feasible with modern GNSS constellations and able to reach accuracies in the order of few decimeters when using proper SISRE compensation techniques.

scholarly journals Estimation of Outdoor PM2.5 Infiltration into Multifamily Homes Depending on Building Characteristics Using Regression Models

2021 ◽  
Vol 13 (10) ◽  
pp. 5708
Author(s):  
Bo-Ram Park ◽  
Ye-Seul Eom ◽  
Dong-Hee Choi ◽  
Dong-Hwa Kang
Keyword(s):  
Regression Analysis ◽  
Correlation Analysis ◽  
Field Test ◽  
Outer Surface ◽  
Regression Models ◽  
Field Tests ◽  
Test Results ◽  
Correlation And Regression Analysis ◽  
Building Characteristics ◽  
Pm2.5 Concentration

The purpose of this study was to evaluate outdoor PM2.5 infiltration into multifamily homes according to the building characteristics using regression models. Field test results from 23 multifamily homes were analyzed to investigate the infiltration factor and building characteristics including floor area, volume, outer surface area, building age, and airtightness. Correlation and regression analysis were then conducted to identify the building factor that is most strongly associated with the infiltration of outdoor PM2.5. The field tests revealed that the average PM2.5 infiltration factor was 0.71 (±0.19). The correlation analysis of the building characteristics and PM2.5 infiltration factor revealed that building airtightness metrics (ACH50, ELA/FA, and NL) had a statistically significant (p < 0.05) positive correlation (r = 0.70, 0.69, and 0.68, respectively) with the infiltration factor. Following the correlation analysis, a regression model for predicting PM2.5 infiltration based on the ACH50 airtightness index was proposed. The study confirmed that the outdoor-origin PM2.5 concentration in highly leaky units could be up to 1.59 times higher than that in airtight units.

Field Test Results in Malaysia Wells of a New-Generation Slim Pulsed Neutron Logging Tool

2017 ◽  
Author(s):  
P. Millot ◽  
F. K. Wong ◽  
D. A. Rose ◽  
T. Zhou ◽  
R. Grover ◽  
...  
Keyword(s):  
Field Test ◽  
Test Results ◽  
New Generation ◽  
Pulsed Neutron ◽  
Logging Tool

High GLR ESP Technologies Comparison, Field Test Results (Russian)

2008 ◽  
Author(s):  
V.G. Bedrin ◽  
M.M. Khasanov ◽  
R.A. Khabibullin ◽  
V.A. Krasnov ◽  
A.A. Pashali ◽  
...  
Keyword(s):  
Field Test ◽  
Test Results ◽  
Comparison Field
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