Global Positioning System Errors Research Articles

Performance Enhancement of Land Vehicle Positioning Using Multiple GPS Receivers in an Urban Area.

The Global Positioning System (GPS) is the most widely used navigation system in land vehicle applications. In urban areas, the GPS suffers from insufficient signal strength, multipath propagation and non-line-of-sight (NLOS) errors, so it thus becomes difficult to obtain accurate and reliable position information. In this paper, an integration algorithm for multiple receivers is proposed to enhance the positioning performance of GPS for land vehicles in urban areas. The pseudoranges of multiple receivers are integrated based on a tightly coupled approach, and erroneous measurements are detected by testing the closeness of the pseudoranges. In order to fairly compare the pseudoranges, GPS errors and terms arising due to the differences between the positions of the receivers need to be compensated. The double-difference technique is used to eliminate GPS errors in the pseudoranges, and the geometrical distance is corrected by projecting the baseline vector between pairs of receivers. In order to test and analyze the proposed algorithm, an experiment involving live data was performed. The positioning performance of the algorithm was compared with that of the receiver autonomous integrity monitoring (RAIM)-based integration algorithm for multiple receivers. The test results showed that the proposed algorithm yields more accurate position information in urban areas.

PPP/INS tightly coupled navigation using adaptive federated filter

Integration of the global positioning system (GPS) with inertial navigation system (INS) has been very intensively studied and widely applied in recent years. Conventional GPS/INS integrated systems that receive pseudorange and Doppler observations can only attain meter-scale accuracy. An INS has also been integrated with double-differenced GPS measurements that remove GPS errors, although this increases system cost. Following the availability of real-time precise orbit and clock products, a precise point positioning PPP/INS tightly coupled navigation system is presented here. Because various types of measurements such as pseudorange, carrier phase and Doppler are available, an adaptive federated filter method is proposed and applied to the PPP/INS integrated system to improve filter efficiency and adaptivity. Provided that the federated local filter and the adaptive filter are equivalent in form, an information allocation factor in the federated filter is constructed based on the adaptive filter factor. Simulation analyses for different INS grades show that the tactical grade INS can provide higher initial value accuracy for PPP. An experiment was performed to validate the new algorithm, and the results indicate that the INS can improve PPP accuracy, especially under challenging positioning conditions. PPP solution accuracy in the east, north and down components can improve by 45, 47 and 24 %, respectively, during partial GPS satellite blockages. The resolution accuracy of the proposed adaptive federated filter is similar to that of a centralized Kalman filter. The proposed method can also realize parallel filter computing and remove the influence of dynamic model errors.

Estimation of Initial Position Using Line Segment Matching in Maps

While navigating in a typical traffic scene, with a drastic drift or sudden jump in its Global Positioning System (GPS) position, the localization based on such an initial position is unable to extract precise overlapping data from the prior map in order to match the current data, thus rendering the localization as unfeasible. In this paper, we first propose a new method to estimate an initial position by matching the infrared reflectivity maps. The maps consist of a highly precise prior map, built with the offline simultaneous localization and mapping (SLAM) technique, and a smooth current map, built with the integral over velocities. Considering the attributes of the maps, we first propose to exploit the stable, rich line segments to match the lidar maps. To evaluate the consistency of the candidate line pairs in both maps, we propose to adopt the local appearance, pairwise geometric attribute and structural likelihood to construct an affinity graph, as well as employ a spectral algorithm to solve the graph efficiently. The initial position is obtained according to the relationship between the vehicle's current position and matched lines. Experiments on the campus with a GPS error of dozens of metres show that our algorithm can provide an accurate initial value with average longitudinal and lateral errors being 1.68m and 1.04m, respectively.

Space-borne BDS receiver for LING QIAO satellite: design, implementation and preliminary in-orbit experiment results

Known as China's first low earth orbit (LEO) mobile communication experimental satellite, the LING QIAO satellite was launched on September 4, 2014. In addition to the two global positioning system (GPS) receivers on board, which are the main navigation receivers supporting the synchronized LEO communication payload and other systems that require navigation input, a BeiDou navigation system (BDS) receiver is also installed on board as an experimental payload. We present the system design and preliminary in-orbit experiment results of the BDS receiver of the LING QIAO satellite. The results show that: (1) The root-mean-square positioning error of the BDS receiver is 13 m, while the GPS error is 1 m; (2) the in-orbit traced service area of BDS is from 55°S to 55°N, 70°E to 150°E, which matches the official announced service area; (3) for industrial-level chips and devices of the BDS payload which are vulnerable to space radiations, the single-event effect monitoring and combating measures, as presented in this work, have been effective for LING QIAO satellite.

Human Mobility Enhances Global Positioning Accuracy for Mobile Phone Localization

Global positioning system (GPS) has enabled a number of geographical applications over many years. Quite a lot of location-based services, however, still suffer from considerable positioning errors of GPS (usually 1 to 20 m in practice). In this study, we design and implement a high-accuracy global positioning solution based on GPS and human mobility captured by mobile phones. Our key observation is that smartphone-enabled dead reckoning supports accurate but local coordinates of users’ trajectories, while GPS provides global but inconsistent coordinates. Considering them simultaneously, we devise techniques to refine the global positioning results by fitting the global positions to the structure of locally measured ones, so the refined positioning results are more likely to elicit the ground truth. We develop a prototype system, named GloCal, and conduct comprehensive experiments in both crowded urban and spacious suburban areas. The evaluation results show that GloCal can achieve 30 percent improvement on average error with respect to GPS. GloCal uses merely mobile phones and requires no infrastructure or additional reference information. As an effective and light-weight augmentation to global positioning, GloCal holds promise in real-world feasibility.

De-noising of GPS structural monitoring observation error using wavelet analysis

In the process of the continuous monitoring of the structure's state properties such as static and dynamic responses using Global Positioning System (GPS), there are unavoidable errors in the observation data. These GPS errors and measurement noises have their disadvantages in the precise monitoring applications because these errors cover up the available signals that are needed. The current study aims to apply three methods, which are used widely to mitigate sensor observation errors. The three methods are based on wavelet analysis, namely principal component analysis method, wavelet compressed method, and the de-noised method. These methods are used to de-noise the GPS observation errors and to prove its performance using the GPS measurements which are collected from the short-time monitoring system designed for Mansoura Railway Bridge located in Egypt. The results have shown that GPS errors can effectively be removed, while the full-movement components of the structure can be extracted from the original signals using wavelet analysis.

Can off-river water and shade provision reduce cattle intrusion into drinking water catchment riparian zones?

Cattle defecation into rivers and overgrazing of riparian zones, are major concerns for drinking water catchment managers. Behaviour modification has been proposed instead of fencing for managing impacts, but reported success varies. Our study aimed to resolve whether provision of off-stream water and shade on real working farms could reduce the likelihood of cattle entering watercourses feeding Sydney, Australia's primary water supply, Lake Burragorang (34°S, 150°E). Cattle herds (1.4 and 11 Animal Unitsha−1) at two sites were fitted with Global Positioning System (GPS) collars (n=12). Cattle movements were tracked following installation of industry-recommended off-stream water and shade (twelve 2 week duration control+treatment experiments). Some statistically significant differences in movement (Mann–Whitney U Prob.<0.0001) were observed, as judged by comparisons of riparian, water trough and shade NEAR distances, and riparian zone visit number, duration and frequency. But effect magnitudes were small, inconsistent between different experiments, and insufficient to justify widespread water and shade provision. These findings contrast with the marked reductions in riparian impacts reported for rangeland pastured (>≈1km2) cattle, but were not inconsistent with smaller scale grazing studies. Statistically significant correlations (Spearman R) were, however, observed (Goulburn_1, Robertson_2 experiments respectively) between the movement of cattle within the same herd (0.94, 0.85), cattle in adjacent fields (0.7, 0.64), and heat stress related factors (temperature, light, humidity, wind) (0.1–0.5) indicating GPS tracking was sound and other factors more strongly influenced animal location. We hypothesize that our off-stream water and shade did not markedly influence cattle movement because our paddocks were relatively small (1.5 and 20ha) compared to rangeland pastures. The study's main limitation was that GPS error prevented differentiation of riparian zone interaction from full stream contact. We recommend in future using direct video to overcome this, and differential quantification of these impacts.

Neural Technologies for Precise Timing in Electric Power Systems with a Single-Frequency GPS Receiver

The global positioning system (GPS), with its ability to provide time synchronization with accuracy to 200 ns over the wide area, provides an ideal tool for performing time tagging in electric power systems. The observed GPS pseudo-range varies from the true range because of range measurement errors. GPS errors sources are ionospheric delays, atmospheric delays, troposphericelays, multi-path effects and dilution of precision etc., affecting the GPS signals as they travel from satellite to user on Earth. In this paper neural models has been presented are for more accurate GPS timing in electric power systems with a single-frequency GPS receiver. The proposed methods use back-propagation (BP), extended Kalman filter (EKF) and particle swarm optimization (PSO) training algorithms, which achieves the optimal training criterion. We use actual data to evaluate the performance of the proposed methods. An experimental test setup is designed and implemented for this purpose. Results using the three methods are discussed. The experimental results obtained from a coarse acquisition (C/A)-code single-frequency GPS receiver are provided to confirm the efficacy of the approaches to give high accurate timing. The GPS timing RMS error using neural network based on PSO learning algorithm reduces to less than 105 and 36 ns, before and after SA, respectively.

Testing the functionality and contact error of a GPS-based wildlife tracking network

Telemetry is a fundamentally important tool for studying animal movements. Traditional telemetry systems have provided time-specific information on locations of individuals; however, recent developments in instruments allow for the tracking of networks of interactions among individuals. Currently, these devices rely on very high frequency (VHF) radio technology, and cannot precisely gauge where interactions occurred without on-site triangulation. Furthermore, although commercially available devices can log successful communication attempts between collars as little as 1 m apart, researchers cannot accurately determine distances within the pre-set detection (contact) threshold of these units. Data loss from proximity-logging devices, and even traditional telemetry devices that are lost or damaged in the field, poses another obstacle to monitoring wildlife social networks. We have developed a prototype proximity-logging Global Positioning System (GPS) collar that offers greater spatial resolution of social interactions, and reduces probability of data loss. In this study, we used captive bighorn sheep (Ovis canadensis) to test the GPS capabilities, contact rates, and contact distance error of our prototype collars. The GPS fix success rate of our collars was >97.93% (n = 95,041; 95% CI = 97.84–98.02%). The collars were communicating with each other about 98% (n = 22,253; 95% CI = 98.50–98.81%) of the time and reciprocal communication occurred 9% of the time. Contact distance error was 9.5 m, which is what would be expected taking into account a baseline GPS spatial error of ±5 m in open environments. The high GPS fix success, low GPS error, and ability to log accurate social interactions with low contact distance error by our prototype collars suggest that the implementation of GPS with proximity-logging technology has the potential to improve currently available social network data. © 2013 The Wildlife Society.

A multipath mitigation discriminator for GPS receiver

A novel technique for mitigating the multipath-induced code delay estimation error in Global Positioning System (GPS) is proposed. In contrast to conventional methods that aim to eliminate multipath signals, the proposed method exploits them to enhance the direct signal without affecting the accuracy of GPS code delay estimates. To achieve this, coherent accumulation of the received GPS signals is first done by transforming the received data into frequency domain and the parameters of multipath signals are then estimated by sparse reconstruction algorithm. Subsequently, a modified local reference signal is employed in delay lock loop (DLL) of the GPS receiver, which mitigates the pseudo-range estimation error and increases the correlation value of direct GPS signal. Simulation results demonstrate the performance and robustness of the proposed method.

Assessment of 5 Hz and 10 Hz GPS units for measuring athlete movement demands

This study aimed to assess the validity and inter-unit reliability of 5 Hz and 10 Hz global positioning system (GPS) units and determine the differences between these units as measures of team sport athlete movement demands. A team sport simulation circuit was completed by eight trained male participants to examine the following movement demands: total distance covered (TD), peak speed and the distance covered, time spent and number of efforts performed at different speed zones. Additionally, repeated high intensity efforts, player load and exertion index were investigated. The analysis using paired samples t-test and percentage typical error of measurement (%TEM) revealed that 10 Hz GPS units provided a valid and reliable measure of TD (p>0.05, %TEM=1.3). The 5 Hz units also displayed reliable measures of TD (%TEM=1.2). It was apparent that as the speed of movement increased, the level of GPS error increased for both 5 Hz and 10 Hz units (%TEM: 0-14%). The updates in GPS firmware and increased sampling rates have further improved the validity and inter-unit reliability of GPS. Consequently, practitioners can more confidently rely on the data obtained from current GPS units during training and matches, however, caution is required when measuring high speed movements.

GPS Output Signal Processing considering both Correlated/White Measurement Noise for Optimal Navigation Filtering

In this paper, a dynamic modeling for the velocity and position information of a single frequency stand-alone GPS(Global Positioning System) receiver is described. In static condition, the position error dynamic model is identified as a first/second order transfer function, and the velocity error model is identified as a band-limited Gaussian white noise via non-parametric method of a PSD(Power Spectrum Density) estimation in continuous time domain. A Kalman filter is proposed considering both correlated/white measurements noise based on identified GPS error model. The performance of the proposed Kalman filtering method is verified via numerical simulation.

Phase Residual Estimations for PCVs of Spaceborne GPS Receiver Antenna and Their Impacts on Precise Orbit Determination of GRACE Satellites

In-flight phase center systematic errors of global positioning system (GPS) receiver antenna are the main restriction for improving the precision of precise orbit determination using dual-frequency GPS. Residual approach is one of the valid methods for in-flight calibration of GPS receiver antenna phase center variations (PCVs) from ground calibration. In this paper, followed by the correction model of spaceborne GPS receiver antenna phase center, ionosphere-free PCVs can be directly estimated by ionosphere-free carrier phase post-fit residuals of reduced dynamic orbit determination. By the data processing of gravity recovery and climate experiment (GRACE) satellites, the following conclusions are drawn. Firstly, the distributions of ionosphere-free carrier phase post-fit residuals from different periods have the similar systematic characteristics. Secondly, simulations show that the influence of phase residual estimations for ionosphere-free PCVs on orbit determination can reach the centimeter level. Finally, it is shown by in-flight data processing that phase residual estimations of current period could not only be used for the calibration for GPS receiver antenna phase center of foretime and current period, but also be used for the forecast of ionosphere-free PCVs in future period, and the accuracy of orbit determination can be well improved.

Interacting Multiple Model Filter-Based Sensor Fusion of GPS With In-Vehicle Sensors for Real-Time Vehicle Positioning

Vehicle position estimation for intelligent vehicles requires not only highly accurate position information but reliable and continuous information provision as well. A low-cost Global Positioning System (GPS) receiver has widely been used for conventional automotive applications, but it does not guarantee accuracy, reliability, or continuity of position data when GPS errors occur. To mitigate GPS errors, numerous Bayesian filters based on sensor fusion algorithms have been studied. The estimation performance of Bayesian filters primarily relies on the choice of process model. For this reason, the change in vehicle dynamics with driving conditions should be addressed in the process model of the Bayesian filters. This paper presents a positioning algorithm based on an interacting multiple model (IMM) filter that integrates low-cost GPS and in-vehicle sensors to adapt the vehicle model to various driving conditions. The model set of the IMM filter is composed of a kinematic vehicle model and a dynamic vehicle model. The algorithm developed in this paper is verified via intensive simulation and evaluated through experimentation with a real-time embedded system. Experimental results show that the performance of the positioning system is accurate and reliable under a wide range of driving conditions.

The Validity and Reliability of 5-hZ Global Positioning System Units to Measure Team Sport Movement Demands

The purpose of this research was to investigate the validity and the reliability of 5-Hz MinimaxX global positioning system (GPS) units measuring athlete movement demands. A team sport simulation circuit (files collected from each unit = 12) and flying 50-m sprints (files collected from each unit = 34) were undertaken, during which the total distance covered; peak speed; player load; the distance covered; time spent and number of efforts performed walking, jogging, running, high-speed running, and sprinting were examined. Movement demands were also separately categorized into low-intensity activity, high-intensity running, and very high-intensity running. The results revealed that GPS was a valid and reliable measure of total distance covered (p > 0.05, percentage typical error of measurement [%TEM] < 5%) and peak speed (p > 0.05, %TEM 5-10%). Further, GPS was found to be a reliable measure of player load (%TEM 4.9%) and the distance covered, time spent, and number of efforts performed at certain velocity zones (%TEM <5% to >10%). The level of GPS error was found to increase along with the velocity of exercise. The findings demonstrated that GPS is capable of measuring movement demands performed at velocities <20 km·h(-1), whereas more caution is to be exercised when analyzing movement demands collected by using GPS velocities >20 km·h(-1).

Simulation of ionospheric error on GPS signals due to changes in the direction of mobile station positions in differential GPS

Ionospheric propagation delay is one of the major sources of error in global positioning system (GPS) precision. Therefore, accurate estimation of the ionospheric delay is required to correct GPS positional calculations. This study investigates the simulation of ionospheric propagation delays of GPS signals caused by changes in the direction of mobile station positions in differential GPS. A comparison was made between short-distance (≤10 km) differential GPS ionospheric errors at rover stations and those at a single base station located at the University Kebangsaan Malaysia (2˚ 55' N, 101˚46'E). In this simulation, four different baseline orientations were considered: North (0˚), East (90˚), South (180˚) and West (270˚) of the University Kebangsaan Malaysia GPS station. Without consideration of any irregularities, the ionospheric errors at the northern rover station were nearly symmetrical to those at the southern station, which is elevation dependent. Similarly, there were nearly symmetrical ionospheric errors for the following orientation pairs: East (90˚) and West (270˚); Northwest (315˚) and Southeast (135˚); and Northeast (45˚) and Southwest (225˚). To maximize the quality of GPS positioning, all ionospheric errors need be considered so that accurate corrections can be made and precision can be enhanced.

Intelligently tuned wavelet parameters for GPS/INS error estimation

This paper presents a new algorithm for de-noising global positioning system (GPS) and inertial navigation system (INS) data and estimates the INS error using wavelet multi-resolution analysis algorithm (WMRA)-based genetic algorithm (GA) with a well-designed structure appropriate for practical and real time implementations because of its very short training time and elevated accuracy. Different techniques have been implemented to de-noise and estimate the INS and GPS errors. Wavelet de-noising is one of the most exploited techniques that have been recently used to increase the precision and reliability of the integrated GPS/INS navigation system. To ameliorate the WMRA algorithm, GA was exploited to optimize the wavelet parameters so as to determine the best wavelet filter, thresholding selection rule (TSR), and the optimum level of decomposition (LOD). This results in increasing the robustness of the WMRA algorithm to estimate the INS error. The proposed intelligent technique has overcome the drawbacks of the tedious selection for WMRA algorithm parameters. Finally, the proposed method improved the stability and reliability of the estimated INS error using real field test data.

The role of ground reference data collection in the prediction of stem volume with LiDAR data in mountain areas

Ground reference data collection represents an important element in the prediction of stem volume with LiDAR-derived variables, and at present it is the most expensive part of such analyses. In this paper two aspects of ground reference data collection were analyzed: (1) the positioning error of the ground plots; and (2) the optimal number of training plots. A system for the prediction of stem volume at area-based level was adopted. LiDAR data were preprocessed and 13 variables describing both height and coverage were extracted. Models were defined using a stepwise ordinary least square (OLS) regression. Three experiments were conducted: (i) the role of the plots positioning error on prediction accuracy; (ii) the influence of random downsampling of plot numbers on prediction accuracy; and (iii) the influence of a stratified downsampling of plot numbers on prediction accuracy based on LiDAR-derived variables. A dataset comprising 799 ground plots was used. They were distributed throughout a mountainous area in the Southern Alps, where the presence of a complex landscape increases the uncertainty of the Global Positioning System (GPS) accuracy, and where a large variety of tree forest species and climatic environments make it necessary to have a large number of sample plots for accurate characterization of the study area. All the experiments provided important indications for LiDAR based forest inventories: the GPS error did not significantly influence the prediction accuracy and it was possible to reduce the number of training samples without compromising the generalization ability of the prediction model. Leading on from these findings, a new ground sampling protocol based on genetic algorithms was proposed. The new protocol allowed us to obtain promising results for the considered dataset: using only 53 training plots, instead of 534 in the original dataset, we obtained the same results for the validation set. These results, obtained in a complex mountainous area, are representative of Alpine environments and allow us to infer that similar (or better) results could also be obtained within non mountainous areas.

ACCURACY OF GPS-TRACKING DEVICES IN EXPOSURE ASSESSMENT STUDIES

Background and Aims: Current lightweight Global Positioning System (GPS) receivers can be used to track an individual’s location over several days to weeks. These tracking devices are increasingly used in epidemiological studies for time-allocation analyses to assess exposure to air pollution or electromagnetic fields. There is little information about the accuracy of these devices and to which extent e.g. different wearing or transport modes affect the performance of a GPS. Methods: We tracked 12 individuals on two days during their commute to work. The tracked routes included different transport modes (walking, biking, car, bus, train). Each individual carried three different GPS tracking devices simultaneously on the left shoulder and a second set of identical GPSs in a backpack. We assessed the performance by first digitizing the true paths travelled by the participants with aerial imagery and subsequently computing the distance of the locations logged by the GPS to the true paths. Results: The median error for walking was 3.7m (IQR 1.6-7.6m), biking 2.9m (IQR 1.3-5.9m), train 4.8m (IQR 2.3-8.9m), bus 4.9m (IQR 2.2-9.5m) and car 3.3m (IQR 1.5-5.9m). A higher accuracy (~1m) was observed for wearing the GPSs on the shoulder versus backpack for the transport modes biking and walking. Although 86% of all logged locations fell within 10m of the true path, about 1% of the errors were larger than 50m. Complete signal loss also occurred, in particular in the train where 28% of the total distance was not tracked. Conclusions: The average accuracy of the GPSs was high. Thus, tracking devices are useful tools for time- and location-based exposure assessment. However, large GPS errors might affect estimates of exposures in the case of exposures with extreme high spatial variability such as radiofrequency fields from mobile phone base stations.

Statistical Analysis of Urban GPS Multipaths and Pseudo-Range Measurement Errors

In urban environments one of the causes of pseudo-range measurement error in Global Positioning System (GPS) is short-delay multipaths due to the scatterers around the receiver. Therefore knowledge of the temporal distribution of GPS multipaths based on a statistical scatterer distribution in an urban environment is essential to estimating the positioning performance and to developing an efficient multipath mitigation technique for urban GPS applications. The work presented here introduces a scatterer distribution model for the urban environment, derives analytical expressions of the consequent time-of-arrival (TOA) probability density function (pdf) with respect to satellite elevation angles, and analyzes the effect of short-delay multipaths on the pseudo-range measurement errors. The expressions derived provide insights into the statistical properties of GPS multipaths and pseudo-range measurement errors in GPS code phase measurement functions due to the short-delay multipaths.