Javascript must be enabled to continue!
Network quality control
View through CrossRef
The aim of computing a geodetic network is to determine the geometry of the configuration of a set of points from spatial observations (e.g. GPS baselines and/or terrestrial measurements). The configuration of points usually consists of newly established points, of which the coordinates still need to be determined, and already existing points, the so-called control points, of which the coordinates are known.
Network quality control deals with the qualitative aspects of network design, network adjustment, network validation and network connection. By means of a network adjustment the relative geometry of the new points is determined and integrated into the geometry of the existing control points. Prior to the network adjustment, the geometry of the network is designed on the basis of precision and reliability criteria.
The adjustment and validation of the overall geometry can be divided in two phases, the free network phase and the connected network phase. In the free network phase, the known coordinates of the control points do not take part in the adjustment and validation. The possible use of a free network phase is based on the idea that a good geodetic network should be sufficiently precise and reliable in itself, without the need of external control. Moreover, it allows one to validate the quality of the external control.
In the connected network phase, the geometry of the free network is integrated into the geometry of the control points. Adjustment and validation in this second phase differs from the free network phase. The adjustment in the second phase is a constrained connection adjustment, since it is often not practical to see the coordinates of the control points change every time a free network is connected to them. For the validation of the connected network however, the unconstrained connected adjustment is used as input. This allows one to take the intrinsic uncertainty of the coordinates of the control points in the connection phase into account.
The goal of this introductory text on network quality control is to convey the necessary knowledge for designing, adjusting and testing geodetic networks. For the purpose of network design, the precision and reliability theory is worked out in detail. This includes the minimal detectable biases and the bias-to-noise ratios. For the purpose of the network adjustment, the principles of unconstrained-, constrained-, and minimally constrained least-squares estimation, are treated. For the network testing, the principles of hypothesis testing are presented and worked out for the different network cases. For the free network phase this includes the overall model test, the w-test, and the data snooping procedure. For the connected network phase, it includes the T-test, with an emphasis on the detection and identification of errors in the control points.
Title: Network quality control
Description:
The aim of computing a geodetic network is to determine the geometry of the configuration of a set of points from spatial observations (e.
g.
GPS baselines and/or terrestrial measurements).
The configuration of points usually consists of newly established points, of which the coordinates still need to be determined, and already existing points, the so-called control points, of which the coordinates are known.
Network quality control deals with the qualitative aspects of network design, network adjustment, network validation and network connection.
By means of a network adjustment the relative geometry of the new points is determined and integrated into the geometry of the existing control points.
Prior to the network adjustment, the geometry of the network is designed on the basis of precision and reliability criteria.
The adjustment and validation of the overall geometry can be divided in two phases, the free network phase and the connected network phase.
In the free network phase, the known coordinates of the control points do not take part in the adjustment and validation.
The possible use of a free network phase is based on the idea that a good geodetic network should be sufficiently precise and reliable in itself, without the need of external control.
Moreover, it allows one to validate the quality of the external control.
In the connected network phase, the geometry of the free network is integrated into the geometry of the control points.
Adjustment and validation in this second phase differs from the free network phase.
The adjustment in the second phase is a constrained connection adjustment, since it is often not practical to see the coordinates of the control points change every time a free network is connected to them.
For the validation of the connected network however, the unconstrained connected adjustment is used as input.
This allows one to take the intrinsic uncertainty of the coordinates of the control points in the connection phase into account.
The goal of this introductory text on network quality control is to convey the necessary knowledge for designing, adjusting and testing geodetic networks.
For the purpose of network design, the precision and reliability theory is worked out in detail.
This includes the minimal detectable biases and the bias-to-noise ratios.
For the purpose of the network adjustment, the principles of unconstrained-, constrained-, and minimally constrained least-squares estimation, are treated.
For the network testing, the principles of hypothesis testing are presented and worked out for the different network cases.
For the free network phase this includes the overall model test, the w-test, and the data snooping procedure.
For the connected network phase, it includes the T-test, with an emphasis on the detection and identification of errors in the control points.
Related Results
Nonlinear optimal control for robotic exoskeletons with electropneumatic actuators
Nonlinear optimal control for robotic exoskeletons with electropneumatic actuators
Purpose
To provide high torques needed to move a robot’s links, electric actuators are followed by a transmission system with a high transmission rate. For instance, gear ratios of...
Network Automation
Network Automation
Purpose: The article "Network Automation in the Contemporary Economy" explores the concepts and methods of effective network management. The application stack, Jinja template engin...
Detection of gene communities in multi-networks reveals cancer drivers
Detection of gene communities in multi-networks reveals cancer drivers
In the past years the advent of high-throughput experimental technologies provided biologists with a flood of molecular data. This huge amount of information requires the design of...
Social Media Use in Neurology: An Analysis of Alzheimer's Information on TikTok with Emphasis on Role of Healthcare Professionals
Social Media Use in Neurology: An Analysis of Alzheimer's Information on TikTok with Emphasis on Role of Healthcare Professionals
Abstract
Introduction
Alzheimer's disease (AD) is the most common neurodegenerative cause of dementia. Social media has become a major source of information for patients and famili...
Network of Intelligent Agents
Network of Intelligent Agents
Any problem-solving can be modelled by actions or methods by which from resources or data, one agent makes an action to obtain a result or arrive at a task. A network of actions ca...
Modified neural networks for rapid recovery of tokamak plasma parameters for real time control
Modified neural networks for rapid recovery of tokamak plasma parameters for real time control
Two modified neural network techniques are used for the identification of the equilibrium plasma parameters of the Superconducting Steady State Tokamak I from external magnetic mea...
Teoria kontroli społecznej w kryminologii
Teoria kontroli społecznej w kryminologii
"Social control" is one of thę most fundamental concepts in sociology. Nevertheless, the meaning of that term has changed, parallel with the development of sociolo...
The synergistic effect of ego-network stability and whole network position: a perspective of transnational coopetition network
The synergistic effect of ego-network stability and whole network position: a perspective of transnational coopetition network
PurposeThe authors selected global automobile manufacturing firms whose sales ranked within 100 in the five years from 2014 to 2018 in the Factiva database to examine how the chara...

