Integration Of Global Positioning System Research Articles

TRANSMITTING GPS AS TEXT FORM THROUGH WIRELESS ON DRONE 2.0

AR Drone 2.0 is a miniature unmanned aerial vehicle used in the field of research. AR Drone 2.0 has a camera and IMU sensors, so it can be used to get visual data from air which hardly done by human. Unmanned aerial vehicle requires GPS (Global Positioning System) so that the pilot can control it remotely as well as to support automatic fly without pilot. While GPS data are supported by AR Drone 2.0 it still needs data conversion process due to the difference in data protocol which is used between the Drone and the GPS. This conversion process required processor or controller to process the data. Integration of GPS with AR Drone using controller also allows drone to run for automatic control. In addition, GPS data will also be sent information of latitude and longitude to computer so that the pilot can determine the current position when AR Drone is flying. The GPS system used for this design can be developed further in the future because it is open source. Â

Context-aware GPS integrity monitoring for intelligent transport systems

The integrity of positioning systems has become an increasingly important requirement for location-based intelligent transport systems (ITS), for example electronic toll collection (ETC), public transport operations and traffic control services. In ITS, satellite navigation systems, such as global positioning system (GPS), are used to provide real-time vehicle positioning information including details of longitude, latitude, direction and speed. Map matching algorithms are used to integrate the positioning information into the digital road map. However, the navigation systems used in ITS cannot provide the high quality positioning information required by most services, due to the various types of errors made in the map matching process and experienced by GPS sensors such as signal outage, and errors due to atmospheric effects and receiver measurement errors, all of which are difficult to measure. An error in the positioning information or map matching process might lead to the inaccurate determination of a vehicle location. This could have legal or economic consequences for ITS applications such as traffic law enforcement systems (e.g., speed fining). Such applications require integrity when measuring the vehicle position and speed information and in the map matching process when locating the vehicle on the correct road segment to avoid errors when charging drivers. Consequently, the integrity algorithm for the navigation system should include a guarantee that the systems do not produce misleading or faulty information as this may lead to significant errors in the ITS services provided. In this paper, a high integrity GPS monitoring algorithm based on the concept of context-awareness that can be applied with real time ITS services to integrate changes in the integrity status of the navigation system was developed. Results suggest that the new integrity algorithm can support various types of location-based ITS services (e.g., route guidance).

A New Technique for Integrating MEMS-Based Low-Cost IMU and GPS in Vehicular Navigation

In providing acceptable navigational solutions, Location-Based Services (LBS) in land navigation rely mostly on integration of Global Positioning System (GPS) and Inertial Navigation System (INS) measurements for accuracy and robustness. The GPS/INS integrated system can provide better land-navigation solutions than the ones any standalone system can provide. Low-cost Inertial Measurement Units (IMUs), based on Microelectromechanical Systems (MEMS) technology, revolutionized the land-navigation system by virtue of their low-cost miniaturization and widespread availability. However, their accuracy is strongly affected by their inherent systematic and stochastic errors, which depend mainly on environmental conditions. The environmental noise and nonlinearities prevent obtaining optimal localization estimates in Land Vehicular Navigation (LVN) while using traditional Kalman Filters (KF). The main goal of this paper is to effectively eliminate stochastic errors of MEMS-based IMUs. The proposed solution is divided into two main components: (1) improving noise cancellation, using advanced stochastic error models in MEMS-based IMUs based on combined Autoregressive Processes (ARP) and first-order Gauss-Markov Process (1GMP), and (2) modeling the low-cost GPS/INS integration, using a hybrid Fuzzy Inference System (FIS) and Second-Order Extended Kalman Filter (SOEKF). The results obtained show that the proposed methods perform better than the traditional techniques do in different stochastic and dynamic situations.

Data collection in field-based environments with alternative mobile devices using mobile geographic information systems and services for quality flexible platforms

The study of various facets of Geographic Information Systems (GIS) and Global Positioning Systems (GPS) integration has been ongoing since the early 1990s. Mobile GIS followed closely behind this with a lag in technological developments for mobile devices such as size, computing power, cost and accessibility. Once mobile technologies caught up to the integration of GIS and GPS through the advent of smart mobile devices of the 21st century, the capabilities were nearly limitless. The integration of GIS and GPS functionality in smart devices started a new era of mobile GIS. This study analyses GPS horizontal positional accuracy for selected smart mobile devices under ideal GPS data collection conditions in reference to National Geodetic Survey benchmark sites within central Arkansas. For the study, smart devices are denied network connectivity to ascertain the robustness of autonomous GPS compared to these same device's assisted-GPS modes and selected dedicated GPS devices. The study area provides many field-based environments that one might typically encounter when collecting location and attribute information such as no connectivity to cellular service, canopy density, urban canyons, and rural and idyllic conditions. Observed differences between study GPS readings and benchmark data are examined statistically to determine each device's effectiveness.

3D displacement field retrieved by integrating Sentinel-1 InSAR and GPS data: the 2014 South Napa earthquake

In this study the integration of Sentinel-1 InSAR (Interferometric Synthetic Aperture Radar) and GPS (Global Positioning System) data was performed to estimate the three components of the ground deformation field due to the Mw 6.0 earthquake occurred on August 24th, 2014, in the Napa Valley, California, USA. The SAR data were acquired by the Sentinel-1 satellite on August 7th and 31st respectively. In addition, the GPS observations acquired during the whole month of August were analyzed. These data were obtained from the Bay Area Regional Deformation Network, the UNAVCO and the Crustal Dynamics Data Information System online archives. The data integration was realized by using a Bayesian statistical approach searching for the optimal estimation of the three deformation components. The experimental results show large displacements caused by the earthquake characterized by a predominantly NW-SE strike-slip fault mechanism.

Data collection in field-based environments with alternative mobile devices using mobile geographic information systems and services for quality flexible platforms

The study of various facets of Geographic Information Systems (GIS) and Global Positioning Systems (GPS) integration has been ongoing since the early 1990s. Mobile GIS followed closely behind this with a lag in technological developments for mobile devices such as size, computing power, cost and accessibility. Once mobile technologies caught up to the integration of GIS and GPS through the advent of smart mobile devices of the 21st century, the capabilities were nearly limitless. The integration of GIS and GPS functionality in smart devices started a new era of mobile GIS. This study analyses GPS horizontal positional accuracy for selected smart mobile devices under ideal GPS data collection conditions in reference to National Geodetic Survey benchmark sites within central Arkansas. For the study, smart devices are denied network connectivity to ascertain the robustness of autonomous GPS compared to these same device's assisted-GPS modes and selected dedicated GPS devices. The study area provides many field-based environments that one might typically encounter when collecting location and attribute information such as no connectivity to cellular service, canopy density, urban canyons, and rural and idyllic conditions. Observed differences between study GPS readings and benchmark data are examined statistically to determine each device's effectiveness.

A Robust Method of Vehicle Stability Accurate Measurement Using GPS and INS

Abstract With the development of the vehicle industry, controlling stability has become more and more important. Techniques of evaluating vehicle stability are in high demand. Integration of Global Positioning System (GPS) and Inertial Navigation System (INS) is a very practical method to get high-precision measurement data. Usually, the Kalman filter is used to fuse the data from GPS and INS. In this paper, a robust method is used to measure vehicle sideslip angle and yaw rate, which are two important parameters for vehicle stability. First, a four-wheel vehicle dynamic model is introduced, based on sideslip angle and yaw rate. Second, a double level Kalman filter is established to fuse the data from Global Positioning System and Inertial Navigation System. Then, this method is simulated on a sample vehicle, using Carsim software to test the sideslip angle and yaw rate. Finally, a real experiment is made to verify the advantage of this approach. The experimental results showed the merits of this method of measurement and estimation, and the approach can meet the design requirements of the vehicle stability controller.

Vision-Aided RAIM: A New Method for GPS Integrity Monitoring in Approach and Landing Phase.

In the 1980s, Global Positioning System (GPS) receiver autonomous integrity monitoring (RAIM) was proposed to provide the integrity of a navigation system by checking the consistency of GPS measurements. However, during the approach and landing phase of a flight path, where there is often low GPS visibility conditions, the performance of the existing RAIM method may not meet the stringent aviation requirements for availability and integrity due to insufficient observations. To solve this problem, a new RAIM method, named vision-aided RAIM (VA-RAIM), is proposed for GPS integrity monitoring in the approach and landing phase. By introducing landmarks as pseudo-satellites, the VA-RAIM enriches the navigation observations to improve the performance of RAIM. In the method, a computer vision system photographs and matches these landmarks to obtain additional measurements for navigation. Nevertheless, the challenging issue is that such additional measurements may suffer from vision errors. To ensure the reliability of the vision measurements, a GPS-based calibration algorithm is presented to reduce the time-invariant part of the vision errors. Then, the calibrated vision measurements are integrated with the GPS observations for integrity monitoring. Simulation results show that the VA-RAIM outperforms the conventional RAIM with a higher level of availability and fault detection rate.

Integration of GIS with Remote Sensing and GPS for Disaster Mitigation

Natural disasters like flood, earthquake, cyclone, volcanic eruption and others are causing immense losses to the property and lives every year. Current status and actual loss information on natural hazards can be determined and also prediction for next probable disasters can be made using different remote sensing and mapping technologies. Global Positioning System (GPS) calculates the exact position of damage. It can also communicate with wireless sensor nodes embedded in potentially dangerous places. GPS provides precise and accurate locations and other related information like speed, track, direction and distance of target objects to emergency responders.Remote Sensing facilitates to map damages without having physical contact with target area. Now with the addition of more remote sensing satellites and other advancements, early warning system is used very efficiently. Remote sensing is being used both at local and global scale. High Resolution Satellite Imagery (HRSI), airborne remote sensing and space-borne remote sensing is playing a vital role in disaster management.Early in Geographic Information System (GIS) was used to collect, arrange, and map the spatial information, but now it has the capability to analyze spatial data. This analytical ability of GIS is the main cause of its adoption by different emergency service providers like the police and ambulance service.The full potential of these so called 3S technologies cannot be used alone. Integration of GPS and other remote sensing techniques with GIS has pointed new horizons in modeling of earth science activities. Many remote sensing cases, including Asian Ocean Tsunami in 2004, Mount Mangart landslides and Pakistan-India earthquake in 2005 are described in this paper.

Integration of GPS precise point positioning and MEMS-based INS using unscented particle filter.

Integration of Global Positioning System (GPS) and Inertial Navigation System (INS) integrated system involves nonlinear motion state and measurement models. However, the extended Kalman filter (EKF) is commonly used as the estimation filter, which might lead to solution divergence. This is usually encountered during GPS outages, when low-cost micro-electro-mechanical sensors (MEMS) inertial sensors are used. To enhance the navigation system performance, alternatives to the standard EKF should be considered. Particle filtering (PF) is commonly considered as a nonlinear estimation technique to accommodate severe MEMS inertial sensor biases and noise behavior. However, the computation burden of PF limits its use. In this study, an improved version of PF, the unscented particle filter (UPF), is utilized, which combines the unscented Kalman filter (UKF) and PF for the integration of GPS precise point positioning and MEMS-based inertial systems. The proposed filter is examined and compared with traditional estimation filters, namely EKF, UKF and PF. Tightly coupled mechanization is adopted, which is developed in the raw GPS and INS measurement domain. Un-differenced ionosphere-free linear combinations of pseudorange and carrier-phase measurements are used for PPP. The performance of the UPF is analyzed using a real test scenario in downtown Kingston, Ontario. It is shown that the use of UPF reduces the number of samples needed to produce an accurate solution, in comparison with the traditional PF, which in turn reduces the processing time. In addition, UPF enhances the positioning accuracy by up to 15% during GPS outages, in comparison with EKF. However, all filters produce comparable results when the GPS measurement updates are available.

Distributed Filtering-Based Autonomous Navigation System of UAV

In this paper, we propose a systematic framework for the autonomous navigation system based on distributed filtering for an Unmanned Aerial Vehicle (UAV). The proposed framework consists of the design and algorithm of the autonomous navigation. Therein, the camera mounted on the UAV functions as a navigation sensor targeted for navigation and positioning. In order to reduce the computational complexity and exclude the risk caused by Global Positioning System (GPS) outage, an autonomous navigation system based on distributed filtering is designed and realized. When GPS is available by monitoring the GPS integrity, sensor information from Strapdown Inertial Navigation System (SINS) and GPS is fused using a 7-state Conventional Kalman Filter (CKF) to estimate the full UAV state (position, velocity and attitude); when GPS is unavailable, sensor information from gyroscopes, accelerometers and magnetometer is fused using a 4-state Extended Kalman Filter (EKF) to estimate the attitude and heading of the UAV, and sensor information from SINS and vision positioning system is fused using a 7-state Incoordinate Interval Kalman Filter (IIKF) to estimate the position and velocity of the UAV. In addition, the second-order vertical channel damping loop is adopted to fuse measurements from a barometer with those of SINS, which suppresses the divergence of the vertical channel error and makes the altitude information calculated by SINS trustable. Both ground and flight experiments of the autonomous navigation system have been carried out. The test results show that the system can provide stabilized attitude information in long durations, and can realize the automatic flight control of UAV.

Study on real-time construction quality monitoring of storehouse surfaces for RCC dams

Abstract Construction quality control of storehouse surfaces is a major concern in the construction management of roller-compacted concrete (RCC) dams. The construction quality of the special dam-filling material RCC is affected by many factors, including the rolling compaction, concrete temperature and construction climate of storehouse surfaces. Timely collection and comprehensive analysis of construction data from the entire construction process of storehouse surfaces and subsequent feedback to construction operations on site are key issues in the control of construction quality and thus obtain greater attention. Traditional methods of construction quality monitoring, including manual inspections or sampling tests, are labor-intensive and easily lead to indiscoverable and irreparable quality defects. In the present study, a real-time construction quality monitoring method for storehouse surfaces of RCC dams is proposed. The auto-identification of compacted layers is realized. Based on the integration of global positioning system (GPS) and global navigation satellite system (GLONASS) techniques, sensor technology and network transmission technology, a real-time construction quality monitoring system is developed and applied to the LDL dam as a case study. The application results realize the whole process, all-weather and timely monitoring of rolling compaction quality, concrete temperature and construction climate in each compacted layer of storehouse surfaces and guarantees satisfactory construction quality and high efficiency, which demonstrates the effectiveness of this system. Moreover, construction data are used to establish predictive models for compactness and the loss of vibrating-compacted (VC) value by applying a multiple regression analysis method.

Tightly Integrated Processing of High-Rate GPS and Accelerometer Observations by Real-Time Estimation of Transient Baseline Shifts

The complementary advantages of high-rate Global Positioning System (GPS) and accelerometer observations for measuring seismic ground motion have been recognised in previous research. Here we propose an approach of tight integration of GPS and accelerometer measurements. The baseline shifts of the accelerometer are introduced as unknown parameters and estimated by a random walk process in the Precise Point Positioning (PPP) solution. To demonstrate the performance of the new strategy, we carried out several experiments using collocated GPS and accelerometer. The experimental results show that the baseline shifts of the accelerometer are automatically corrected, and high precision coseismic information of strong ground motion can be obtained in real-time. Additionally, the convergence and precision of the PPP is improved by the combined solution.

Assessment and Mitigation of Ionospheric Disturbance Effects on GPS Accuracy and Integrity

Ionospheric disturbances affect Global Positioning System (GPS) performance in terms of accuracy and integrity, especially over the equatorial region. During the period of the disturbances, GPS receivers suffer from a high noise level. Not taken into account by the current stochastic model, the ionospheric disturbances degrade GPS positioning accuracy. In addition, non-Gaussian tails are observed in the distribution of the noise during the period of the disturbances; therefore the integrity of GPS can also be affected. This paper develops a statistical solution that is able to mitigate effects of ionospheric disturbances on GPS accuracy and integrity using a commercial dual frequency receiver. The Rate of Total Electron Content (TEC) change Index (ROTI), a parameter derived from the dual frequency receiver, is used to group the levels of ionospheric disturbances. The standard deviations of the pseudorange noise under different groups are evaluated. By incorporating both the ROTI and the satellite elevation, a modified stochastic model is proposed to reduce the effect of the disturbed observation on the positioning accuracy. The performance of the model is evaluated by a test and an inflated sigma for each group is recommended for over-bounding anomalies of observations to protect the user against threats from ionospheric disturbances. This technique, together with results in this paper, can be applied to mitigate the effects of ionospheric disturbances on GPS.

PMI based nonlinear H∞ estimation of unknown sensor error for INS/GPS integrated system

This paper deals with the problem of robust estimation for time-varying sensor errors of inertial navigation system (INS) and global positioning system integration. A nonlinear strapdown INS error model is established to describe the behavior of the the integrated system. Under assumptions of time-varying bias and noise being L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> norm bounded, a robust H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> nonlinear estimator by Krein space theory is proposed and, based on this, a proportional and multi-integral H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> estimator is developed for simultaneous estimation of the navigation states and sensor errors. Finally, a flight experiment is implemented to show the effectiveness of the proposed method.

An INS-Aided Tight Integration Approach for Relative Positioning Enhancement in VANETs

Relative positioning among vehicles is a fundamental parameter for advanced applications of intelligent transportation systems such as collision avoidance and road safety. However, the level of positioning accuracy achievable using Global Navigation Satellite Systems does not meet the requirements of these applications. Cooperative positioning (CP) techniques can be used for improving the performance of absolute or relative positioning in a vehicular ad hoc network (VANET). The tight integration of Global Positioning System (GPS) data among communicating vehicles has already been introduced by the authors as a tight CP approach with specific advantages over differential GPS (DGPS) for relative positioning. In this paper, we propose an enhanced tight CP technique adding low-cost inertial navigation sensors and GPS Doppler shifts. Based on analytical and experimental results, the new method outperforms its predecessor and DGPS by 10% and 24%, respectively.

A novel approach to the integration of GPS and INS using recurrent neural networks with evolutionary optimization techniques

Integration of Global Positioning System (GPS) and Inertial Navigation System (INS) has been extensively used in aircraft applications like autopilot, to provide better navigation, even in the absence of GPS. Even though Kalman Filter (KF) based GPS–INS integration provides a robust solution to the navigation, it requires prior knowledge of the error model of INS, which increases the complexity of the system. Hence Neural Networks (NN) based GPS–INS integration are available in the literature. But the NN based solutions have problems such as convergence and inaccuracy. To get better convergence ability, the Recurrent Neural Networks such as Elman and Jordan Neural Networks are proposed. Normally Back Propagation Algorithm (BPA) is used to train the Recurrent Neural Network. But BPA has disadvantages such as slow convergence rate and inaccuracy due to local minima. To overcome these problems, Evolutionary Algorithm based Recurrent Neural Network (EARNN) is proposed to get better positional accuracy of the target. In this work, the integration of GPS and INS based on various Neural Networks like Back Propagation Neural Network (BPNN), Elman Neural Network and Jordan Neural Network using BPA, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) is also analyzed and their performance parameters like Mean Absolute Error (MAE), R-Square, Root Mean Square Error (RMSE), Performance Index (PI), Sensitivity Index (SI), Training time of the networks and the number of epochs are compared.

Integration of INS, GPS, Magnetometer and Barometer for Improving Accuracy Navigation of the Vehicle

This paper describes integrated navigation system that is based on a low cost inertial sensor, global positioning system (GPS) receiver, magnetometer and a barometer, in order to improve accuracy of complete attitude and navigation solution. The main advantage of integration consists in availability of reliable navigation parameters during the intervals of absence of GPS data. The magnetometer and the barometer are applied for the attitude calibration and vertical channel stabilization, respectively. The acceptable accuracy of inertial navigation system (INS) is achieved by the proper damping of INS errors. The integration is made by the implementation of an extended Kalman filter (EKF) with control signal that is designed appropriate for low accuracy sensors noise characteristics. The analysis of integrated navigation system performances is made experimentally and the results show that integrated navigation system provides continuous and reliable navigation solutions. Defence Science Journal, 2013, 63(5), pp.451 -455 , DOI:http://dx.doi.org/10.14429/dsj.63.4534

Early warning of illegal development for protected areas by integrating cellular automata with neural networks

Ecological security has become a major issue under fast urbanization in China. As the first two cities in this country, Shenzhen and Dongguan issued the ordinance of Eco-designated Line of Control (ELC) to “wire” ecologically important areas for strict protection in 2005 and 2009 respectively. Early warning systems (EWS) are a useful tool for assisting the implementation ELC. In this study, a multi-model approach is proposed for the early warning of illegal development by integrating cellular automata (CA) and artificial neural networks (ANN). The objective is to prevent the ecological risks or catastrophe caused by such development at an early stage. The integrated model is calibrated by using the empirical information from both remote sensing and handheld GPS (global positioning systems). The MAR indicator which is the ratio of missing alarms to all the warnings is proposed for better assessment of the model performance. It is found that the fast urban development has caused significant threats to natural-area protection in the study area. The integration of CA, ANN and GPS provides a powerful tool for describing and predicting illegal development which is in highly non-linear and fragmented forms. The comparison shows that this multi-model approach has much better performances than the single-model approach for the early warning. Compared with the single models of CA and ANN, this integrated multi-model can improve the value of MAR by 65.48% and 5.17% respectively.

Integration of GPS with a WiFi high accuracy ranging functionality

High accuracy seamless positioning is required to support a vast number of applications in varying operational environments. Over the last few years, the global positioning system (GPS) has become the de facto technology for positioning applications. However, its performance is limited in indoor and dense urban environments due to multipath as well as signal attenuation and blockage. A number of techniques integrating GPS with other positioning technologies have been developed to address the limitations of standalone GPS in these difficult environments. While most of the developed techniques cover the outages of GPS in such environments, they do not provide acceptable performance, in terms of positioning accuracy, especially for some mission-critical (e.g. safety) applications. This paper proposes a tightly coupled (i.e. in the measurement domain) GPS/WiFi integration method which, in addition to addressing GPS outages, improves the overall positioning accuracy to the meter-level, thus satisfying the requirements of a number of location based services and intelligent transport systems applications. The performance of the proposed GPS/WiFi integration method is assessed for a number of scenarios in a simulation environment for an identified dense urban area in London, UK.