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A simplified global positioning system (GPS) error model including models for a variety of abnormal operational conditions and failures is developed to provide simulation tools for the design, testing, and evaluation of autonomous flight fault tolerant control laws. The paper aims to discuss these issues.

Analysis and experimental data are used to build simplified models for GPS position and velocity errors on all three channels. The GPS model is interfaced with West Virginia University unmanned aerial vehicles (UAV) simulation environment and its utility demonstrated through simulation for several autonomous flight scenarios including GPS abnormal operation.

The proposed simplified GPS model achieves desirable levels of accuracy and realism for all components for the purpose of general UAV dynamic simulation and development of fault tolerant autonomous flight control laws.

The simplified GPS model allows investigating GPS malfunction effects on the performance of autonomous UAVs and designing trajectory tracking algorithms with advanced fault tolerant capabilities.

The simplified GPS model has proved to be a flexible and useful tool for UAV simulation and design of autonomous flight control laws at normal and abnormal conditions.

The outcomes of this research effort achieve a level of detail never attempted before in modeling GPS operation at normal and abnormal conditions for UAV simulation and autonomous flight control laws design using a simplified framework.

The purpose of this paper is to discuss the applicability of Global Positioning System (GPS) ionospheric delay correction models. Ionospheric delay is the most influential error source in GPS positioning, and ionospheric refraction is difficult to be corrected by dual frequency measurement for the common single frequency GPS receivers. Generally, ionospheric models are employed to correct errors. In order to analyze the ionospheric influence to GPS signals and the accuracy and adaptability of GPS ionospheric error correction models a quantificational analysis for ionospheric error correction models is absolutely necessary.

On the base of the mechanism of ionospheric error, the Klobuchar model that is widely used and actual measured correction model (including local and global ionospheric error correction models) are analyzed in detail. With the data about ionosphere obtained from GPS authority Crustal Dynamics Data Information System, the precision and adaptability of two kinds of ionospheric error correction model are validated, and a predigested method of investigating precision of local ionospheric error correction model is presented.

Klobuchar model has higher precision in middle or low latitude than in high latitude, and ionospheric delay fluctuates acutely in a day with a day‐cycle. Ionospheric delay varies as the latitude changes: ionospheric delay is largest around equator and smallest in the areas of two poles, which shows symmetry. The relationship between ionospheric delay and longitude is similar to the relationship between ionospheric delay and latitude. The fitting model has better effect than Klobuchar model.

This paper thoroughly researches GPS ionospheric error correction models. The conclusions are presented for the selection of GPS correction models, that it is useful for practical engineering application and will be the theoretic foundation for the improvement of the GPS accurate positioning.

The purpose of this paper is to improve the tracking performance of the carrier phase lock loop (PLL) in the strapdown inertial navigation system/global positioning system (SINS/GPS) integrated system with an innovative scheme of ultra‐tight integration.

First, providing the Doppler frequency for PLL using SINS velocity could enlarge the loop equivalent bandwidth and reduce the dynamic effect on the carrier loop. Meanwhile, lowering the filter bandwidth could increase the immunity to noise. Second, the relationships between the PLL and SINS errors have been analyzed, and then the PLL error model is established to eliminate the correlation between the pseudo‐range‐rate error and SINS velocity error. Third, the carrier frequency is regulated to improve the tracking accuracy, according to the error estimations of Kalman filter.

The innovative ultra‐tightly integrated system could not only enhance the anti‐jamming capability and the dynamic tracking performance of the tracking loops, but also improve the pseudo‐range‐rate measurements accuracy for the integrated filter.

This paper provides further study on the method of enhancing the carrier‐tracking performance and improving the integration mode in the ultra‐tightly integrated system based on the software‐defined GPS receiver.

The healthcare sector faces new financial and managerial accountability demands, along with their clinical accountability. Various studies show strong opposition by clinicians to new accountability tools, new structures and new ways of working. Less attention is paid to the innovative roles doctors can play in leading changes that use new managerial tools and techniques. The purpose of this paper is to analyse two original case studies illustrating how general practitioners (GPs) in Germany have led radical change.

The paper draws upon original research in Germany to present two case studies using a qualitative method, which are analysed using Glaser and Strauss' conventions of grounded theory, structured by Wenger's communities of practice framework, supporting a comprehensive literature review.

GPs are found to be able to lead radical change in healthcare delivery models and organisation using entrepreneurial talents developed in their practice businesses and to embrace modernising tools and techniques and in the process redefine their identities to include management process in addition to medical competences.

The paper presents two original case studies of radical change leading to an integration of healthcare services in Germany. The approach adopted by the German GPs reveals important general lessons for practitioners, as does the analytical framework employed in the paper.

The purpose of this paper is to present fusion of inertial navigation system (INS) and global positioning system (GPS) for estimating position, velocities, attitude and heading of an unmanned aerial vehicle (UAV).

A 15-state extended Kalman filter (EKF) and a split architecture consisting of six-state nonlinear complementary filter (NCF) and nine-state EKF are investigated in detail. In both these fusion architectures GPS and inertial measurement unit consisting of three axis accelerometers, three axis rate gyros and three axis magnetometer have been fused in open loop fashion (loosely coupled) to estimate the navigation states.

These architectures have been implemented in MATLAB/SIMULINK environment and evaluated in closed loop guidance of Black-Kite MAV with software-in-the-loop-simulation (SILS) setup. Furthermore, both the algorithms are validated with flight test data obtained from on-board data logger using an off-the shelf autopilot board (Ardupilot Mega APM-2.5) on SLYBIRD UAV.

The proposed architectures are of high value to accomplish INS/GPS fusion, which plays a vital role in autonomous guidance and navigation of an UAV.

In a global positioning system (GPS) receiver, one of the most time-consuming tasks is to identify and track the visible satellites. The paper aims to propose and examine in detail new and shorter identification patterns or lite pseudo-codes – pseudo-random numbers (PRNs) – that allow GPS receivers to reduce dramatically the computational effort to identify and track GPS satellites. Obtaining lite pseudo-codes is a multi-objective optimization problem that the paper resolves using genetic algorithms (GAs).

The lite PRNs are obtained by using NSGA-II and omni-optimizer multi-objective optimization techniques.

The new PRNs obtained with the proposed single/multi-objective solutions are always better than previously presented when the highest detection peak (DP) is required for the GPS receiver.

Results demonstrate that the problem of “obtaining lite pseudo-codes” is a multi-objective optimization problem. In other words, the solutions obtained with the single-objective approach could belong to a local minimum. The multi-objective approach provides a better solution than the single-objective approach in a 37 percent of the satellites while in other cases the multi-objective approach reaches the same DPs with a similar noise.

This paper aims to present a highly accessible and affordable tracking model for earthmoving operations in an attempt to overcome some of the limitations of current tracking models.

The proposed methodology involves four main processes: acquiring onsite terrestrial images, processing the images into 3D scaled cloud data, extracting volumetric measurements and crew productivity estimations from multiple point clouds using Delaunay triangulation and conducting earned value/schedule analysis and forecasting the remaining scope of work based on the estimated performance. For validation, the tracking model was compared with an observation-based tracking approach for a backfilling site. It was also used for tracking a coarse base aggregate inventory for a road construction project.

The presented model has proved to be a practical and accurate tracking approach that algorithmically estimates and forecasts all performance parameters from the captured data.

The proposed model is unique in extracting accurate volumetric measurements directly from multiple point clouds in a developed code using Delaunay triangulation instead of extracting them from textured models in modelling software which is neither automated nor time-effective. Furthermore, the presented model uses a self-calibration approach aiming to eliminate the pre-calibration procedure required before image capturing for each camera intended to be used. Thus, any worker onsite can directly capture the required images with an easily accessible camera (e.g. handheld camera or a smartphone) and can be sent to any processing device via e-mail, cloud-based storage or any communication application (e.g. WhatsApp).

The purpose of this paper is to develop and implement a general sensor model under normal and abnormal operational conditions including nine functional categories (FCs) to provide additional tools for the design, testing and evaluation of unmanned aerial systems within the West Virginia University unmanned air systems (UAS) simulation environment.

The characteristics under normal and abnormal operation of various types of sensors typically used for UAS control are classified within nine FCs. A general and comprehensive framework for sensor modeling is defined as a sequential alteration of the exact value of the measurand corresponding to each FC. Simple mathematical and logical algorithms are used in this process. Each FC is characterized by several parameters, which may be maintained constant or may vary during simulation. The user has maximum flexibility in selecting values for the parameters within and outside sensor design ranges. These values can be set to change at pre-defined moments, such that permanent and intermittent scenarios can be simulated. Sensor outputs are integrated with the autonomous flight simulation allowing for evaluation and analysis of control laws.

The developed sensor model can provide the desirable levels of realism necessary for assessing UAS behavior and dynamic response under sensor failure conditions, as well as evaluating the performance of autonomous flight control laws.

Due to its generality and flexibility, the proposed sensor model allows detailed insight into the dynamic implications of sensor functionality on the performance of control algorithms. It may open new directions for investigating the synergistic interactions between sensors and control systems and lead to improvements in both areas.

The implementation of the proposed sensor model provides a valuable and flexible simulation tool that can support system design for safety purposes. Specifically, it can address directly the analysis and design of fault tolerant flight control laws for autonomous UASs. The proposed model can be easily customized to be used for different complex dynamic systems.

In this paper, information on sensor functionality is fused and organized to develop a general and comprehensive framework for sensor modeling at normal and abnormal operational conditions. The implementation of the proposed approach enhances significantly the capability of the UAS simulation environment to address important issues related to the design of control laws with high performance and desirable robustness for safety purposes.

The purpose of this paper is an investigation of driving behaviour and impacts on emissions at two traffic junctions.

A signalised junction and a roundabout in Edinburgh have been selected. An instrumented car has been used and a GPS to monitor driving activities as well as a gas analyser to monitor the vehicle's emissions during the evening peak hour.

Vehicles’ emissions are affected by a large number of factors including characteristics of the engine and the vehicle, characteristics of the road, the fuel used and driving behaviour.

Different methods and approaches have been used to investigate the behaviour of vehicles at various traffic junctions. The main aim, however, has mostly been to reduce travel times as well as traffic delays and queues at the junction. Consideration of environmental impacts has also been made, but often as a by‐product of congestion reduction and not as a main aim.

Troposphere delay is one of the important error sources in global positioning system (GPS) positioning. The purpose of this paper is to analyze the accuracy and adaptability of GPS troposphere error correction models, and to provide theoretic foundation for model selection in GPS accurate positioning.

The principle of troposphere delay error effecting on GPS signals is theoretically analyzed. The model peculiarity and modeling method of the four common troposphere delay correction models: Hopfield, Saastamoinen, Black, and Egnos models are discussed detailedly. With the measurement data from Crustal Dynamics Data Information System of the technical support institution for GPS, the accuracy and applicability of the four models are quantificationally studied.

For a low elevation, Hopfield, Saastamoinen, and Black models show great agreement with each other, and have quite high precision. In the zenith direction, the maximal troposphere delay error of three models are all less than 1 dm, but Black and Hopfield models have higher precision than Saastamoinen model. Black model can be regarded as the improved form of Hopfield model: for a high elevation, precision of two models are close, while for a low elevation, Black model shows to be more effective than Hopfield model. The precision of Egnos model is quite lower than that of Black, Hopfield, and Saastamoinen models. However, Egnos model can be a better choice when it is difficult to obtain real‐time meteorological data in certain application environment.

This paper makes thorough research on GPS troposphere delay error correction models. The conclusions are presented for selecting troposphere delay models, which are useful for practical engineering application.