Global Positioning System Errors Research Articles

Development of vehicle- recognition method on water surfaces using LiDAR data: SPD2 (spherically stratified point projection with diameter and distance)

Swarm robot systems are an important application of autonomous unmanned surface vehicles on water surfaces. For monitoring natural environments and conducting security activities within a certain range using a surface vehicle, the swarm robot system is more efficient than the operation of a single object as the former can reduce cost and save time. It is necessary to detect adjacent surface obstacles robustly to operate a cluster of unmanned surface vehicles. For this purpose, a LiDAR (light detection and ranging) sensor is used as it can simultaneously obtain 3D information for all directions, relatively robustly and accurately, irrespective of the surrounding environmental conditions. Although the GPS (global-positioning-system) error range exists, obtaining measurements of the surface-vessel position can still ensure stability during platoon maneuvering. In this study, a three-layer convolutional neural network is applied to classify types of surface vehicles. The aim of this approach is to redefine the sparse 3D point cloud data as 2D image data with a connotative meaning and subsequently utilize this transformed data for object classification purposes. Hence, we have proposed a descriptor that converts the 3D point cloud data into 2D image data. To use this descriptor effectively, it is necessary to perform a clustering operation that separates the point clouds for each object. We developed voxel-based clustering for the point cloud clustering. Furthermore, using the descriptor, 3D point cloud data can be converted into a 2D feature image, and the converted 2D image is provided as an input value to the network. We intend to verify the validity of the proposed 3D point cloud feature descriptor by using experimental data in the simulator. Furthermore, we explore the feasibility of real-time object classification within this framework.

Adaptive Ascent Control of a Collaborative Object Transportation System Using Two Quadrotors.

The paper focuses on the issue of collaborative control of a two quadrotor (Unmanned Aerial Vehicle QDR) system. In particular, two quadrotors perform the task of horizontally transporting a long payload along a predefined trajectory. A leader–follower method is used to synchronize the motion of both QDRs. Conventional PD controllers drive the motion of the leader QDR-L to follow a predefined trajectory. To control a follower QDR-F drive, in the case of indoor applications, a Position Feedback Controller approach (PFC) can be used. To control the QDR-F, the PFC system uses the position information of QDR-L and the required accurate tracking cameras. In our solution, outdoor applications are considered, and usage of the Global Positioning System (GPS) is needed. However, GPS errors can adversely affect the system’s stability. The Force Feedback Controller approach (FFC) is therefore implemented to control the QDR-F motion. The FFC system assumes a rigid gripping of payload by both QDRs. The QDR-F collaborative motion is controlled using the feedback contact forces and torques acting on it due to the motion of the QDR-L. For FFC implementation, the principle of admittance control is used. The admittance controller simulates a virtual “mass-spring-damper” system and drives the motion of the QDR-F according to the contact forces. With the FFC control scheme, the follower QDR-F can be controlled without using the QDR-L positional feedback and the GPS. The contribution to the quality of payload transportation is the novelty of the article. In practice, one of the requirements may be to maintain the horizontal position of the payload. In this paper, an original solution is presented to minimize the horizontal position difference of both QDRs. A new procedure of the transfer admittance controller adaptation according to the mass of the transported payload is designed. The adaptive admittance FFC system is implemented in a Matlab-Simulink environment. The effectiveness of its trajectory tracking and horizontal stabilization functions for variations of the payload mass are demonstrated by numerical calculations.

Testing Patch, Helix and Vertical Dipole GPS Antennas with without Choke Ring Frame

Abstract: The antenna is the connecting part between Global Positioning System (GPS) satellite and receiver, and it works on transferring the satellite signal propagation to the receiver with minimum interruption. This is beside the main role that GPS antenna can play for accurate and precise positioning. High quality GPS antennas have high gain, systematic radiation pattern, Right Hand Circular Polarization (RHCP), and low directivity. Multipath error is a principal error source connected with GPS positioning and it can be mitigated using better antenna design. GPS antennas tend to be provided with choke ring frame for protection from the reflected RHCP electromagnetic waves. In this paper, the performance of three different types of GPS antenna, namely: Microstrip Patch Antenna (MPA), Helix antenna (HA), and Vertical Dipole antenna (VDA), with and without choke ring frame, has been investigated using single frequency GPS receiver. The antennas have been examined under the same GPS environment, including satellites constellation, geometry, and surroundings, with different heights from the ground reflector surface for multipath evaluation. Short base-line kinematic differential code GPS (DGPS) technique has been used for removing and mitigating the main GPS error sources and focusing on multipath effect. Satellite residuals and the quality of the obtained positioning have been considered for evaluating the performance of each antenna. The results have reflected the ability of choke ring frames to improve the performance of the three GPS antennas with different rates in multipath areas, and illustrate the advantages and limitations of each antenna. Keywords: Micro-strip, Patch, Helix, Vertical Dipole, GPS, Choke Ring Antenna, Multipath.

Sistem Monitoring Konversi Wifi-RSSI Menjadi Jarak Antar Device dan Router Menggunakan Node-Red Pada Pengembangan Sistem Navigasi Dalam Ruang

A global positioning system (GPS) is often used as a tool for determining direction and positioning. However, GPS is weak in estimating the error, which is quite large at the actual distance, so positioning in a narrow area cannot work well. Therefore, a better method is needed to be used as a narrow-navigation system. This study aimed to determine whether RSSI has a better error than GPS for navigation devices in a narrow space or area. The method used was plotting graphs using Ms. Excel and determining the value of R square. The RSSI value was obtained through the Esp8266 device and then sent to the server via the MQTT protocol and converted using Node-Red on the Raspberry Pi. The comparison results of the Esp8266 device RSSI graph match the Laptop RSSI device as a reference. The match between the relative distance to the RSSI with the actual distance shows a 3.3-33% error. This result is considered much better than the GPS error.

Modelling and Analysis of GPS Error Effect on the Zone and Energy Aware-AODV (ZEA-AODV) Protocol

The Zone and Energy Aware protocol based on the Ad hoc On demand Distance Vector (AODV) protocol (ZEA-AODV) provides a superior performance compared to the classic AODV protocol when number of nodes is less than 70 [1]. ZEA-AODV protocol is using location and energy information to reduce energy consumption and routing overhead. It is a combination of the two proposed protocols, Zone-aware AODV (Z-AODV) protocol and Energy-Balanced AODV (EB-AODV) protocol. In [1], we assumed that each host knows its current location precisely. In this paper, Global Positioning System (GPS) error is considered, therefore, the standard GPS error is modelled by generating two samples for movement of a terminal by simulation. One of the movements is as a simulation of the actual movement, and the other one as a simulation of the error. Then both movements are provided to the terminal. This GPS error modelling is considered in the case of ZEA-AODV protocol to investigate the effect of such error on its performance. Using the simulation, it is found that, although the overall performance of ZEA-AODV protocol is degraded when GPS error is considered, but energy conservation is noticeably still better than that of normal AODV protocol.

Vertical Displacements of the Amazon Basin From GRACE and GPS

AbstractThe extent to which Gravity Recovery and Climate Experiment (GRACE)‐recovered gravity anomalies can improve our understanding of Global Positioning System (GPS)‐measured vertical displacements is currently uncertain. To address this issue, we compared vertical displacements measured by 23 GPS stations in the Amazon basin with displacements estimated from GRACE geopotential fields. We show that despite poor correlation ( ) between rate estimates in GPS and GRACE‐derived displacement time series, further analyses reveal low bias between annual amplitude estimates and a scaling near 1. There is higher correlation ( ) between annual periodic motions, with near 1 to 1 agreement, but there is poor correlation ( ) and little agreement between semiannual amplitude estimates. Subtracting GRACE displacements from the GPS time series flattens the GPS power spectra, reducing the spectral index magnitude, on average, from (“fractional Brownian motion”) to (“fractional Gaussian noise”), suggesting that some fraction of the apparent GPS error correlation derives from mass loading signals that are not completely characterized by secular trends or seasonal periodic motions. From March 2011 to November 2016, we find a GPS and GRACE‐derived displacement combined average uplift of the Amazon basin of mm/yr and combined average annual periodic motion of mm. Deviations from a standard trajectory model for site motion are apparent in both data sets and appear to coincide with various flooding and drought events between 2011 and 2016, which suggests that the GPS coordinate time series record displacements driven by large‐scale climate oscillations.

A Novel Unscented Kalman Filter Strategy To Enhance Navigation System Performance

The Extended Kalman Filter (EKF) is the most widely estimation algorithm used for nonlinear system such as a navigation system to fuse an inertial navigation system (INS) with Global Positioning System (GPS) which its information has complementary nature to get more accurate navigation information. Unfortunately, the performance of INS/GPS fusion using EKF is degraded due to the linearization error and GPS error. Therefore, a new algorithm is developed to overcome these issues. This algorithm uses the sampling-based Unscented Kalman Filter (UKF) to solve the linearization problem, and ignore the GPS reading when there is a large error in its measurements. The new algorithm is named Adaptive Loosely Coupled Unscented Kalman Filter (ALCUKF). The ALCUKF-based INS/GPS systems are presented for two different datasets. The first dataset is acquired using a high-end tactical-grade SPAN unit featuring Novatel HG1700 IMU module. The second dataset is acquired from a MEMS-based SCC1300-D04 IMU unit from VTI. The results of the new method are compared against reference ground truth trajectories and measured quantitatively using the Root Mean Square Error (RMSE). The ALCUKF increased the navigation system performance significantly when compared with EKF for both datasets as shown in the paper.

Vehicle Position Correction: A Vehicular Blockchain Networks-Based GPS Error Sharing Framework

The positioning accuracy of the existing vehicular Global Positioning System (GPS) is far from sufficient to support autonomous driving and ITS applications. To remedy that, leading methods such as ranging and cooperation have improved the positioning accuracy to varying degrees, but they are still full of challenges in practical applications. Especially for cooperative positioning, in addition to the performance of methods, cooperators may provide false data due to attacks or selfishness, which can seriously affect the positioning accuracy. By fully exploiting the characteristics of blockchain and edge computing, this paper proposes a vehicular blockchain-based secure and efficient GPS positioning error evolution sharing framework, which improves vehicle positioning accuracy from ensuring security and credibility of cooperators and data. First, by analyzing the GPS error, a bridge can be established between the sensor-rich vehicles and the common vehicles to achieve cooperation by sharing the positioning error evolution at a specific time and location. Particularly, the positioning error evolution is obtained by a deep neural network (DNN)-based prediction algorithm running on the edge server. We further propose to use blockchain technology for storage and sharing the evolution of positioning errors, mainly to guarantee the security of cooperative vehicles and mobile edge computing nodes (MECNs). In addition, the corresponding smart contracts are designed to automate and efficiently perform storage and sharing tasks as well as solve inconsistencies in time scales. Extensive simulations based on actual data indicate the accuracy and security of our proposal in terms of positioning error correction and data sharing.

Reduction of Ionosphere Error in GPS for Location Identity

The Global Positioning System (GPS) has moved toward becoming an amazing tool for ionospheric contemplates. The accuracy of coordination data for the Global Positioning System (GPS) receiver by the GPS monitoring station ought to be expanded. The accuracy can be expanded by deciding the sources of the disturbances that have been delivered by all of ionospheric impacts. The ionospheric delay within the propagation of GPS signal is one among the primary sources of error in GPS precise positioning. The objective of this survey paper is to identify the common factors that affect the accuracy of GPS and identify an effective method which could mitigate or overcome most of those factors. The main objective is the improvement of the measurement accuracy by this correction of ionosphere delay.

Cooperative Localization Improvement Using Distance Information in Vehicular Ad Hoc Networks.

In vehicular ad hoc networks (VANets), a precise localization system is a crucial factor for several critical safety applications. The global positioning system (GPS) is commonly used to determine the vehicles’ position estimation. However, it has unwanted errors yet that can be worse in some areas, such as urban street canyons and indoor parking lots, making it inaccurate for most critical safety applications. In this work, we present a new position estimation method called cooperative vehicle localization improvement using distance information (CoVaLID), which improves GPS positions of nearby vehicles and minimize their errors through an extended Kalman filter to execute Data Fusion using GPS and distance information. Our solution also uses distance information to assess the position accuracy related to three different aspects: the number of vehicles, vehicle trajectory, and distance information error. For that purpose, we use a weighted average method to put more confidence in distance information given by neighbors closer to the target. We implement and evaluate the performance of CoVaLID using real-world data, as well as discuss the impact of different distance sensors in our proposed solution. Our results clearly show that CoVaLID is capable of reducing the GPS error by 63%, and 53% when compared to the state-of-the-art VANet location improve (VLOCI) algorithm.

Multistep weighted least squares estimation method for improving single-point positioning accuracy

The integration of multiple Global Navigation Satellite Systems (GNSSs), such as the Chinese BeiDou Navigation Satellite System (BDS) and the American Global Positioning System (GPS), makes it possible to improve the accuracy of single-point positioning (SPP). However, the current accuracy of GNSS SPP with code measurement is on the order of several meters. In order to further improve SPP accuracy, we develop a multistep weighted least squares (MWLS) estimation method based on the elevation-dependent weighted least squares (EWLS) method. In this approach, the weight of each visible satellite is determined by its elevation and azimuth, and the coordinate components are semi-independently and separately calculated by MWLS. We conduct open-sky and blocked static tests as well as a vehicle-based kinematic test using single-frequency receivers to assess the effectiveness of this approach. Comparing the positioning errors of GPS and GPS/BDS SPPs between the MWLS and EWLS methods shows that the former improved the positioning accuracy more than the customary latter. Specifically, MWLS achieves a horizontal accuracy of about 1 m and a positional accuracy of 2 to 3 m in clear observational environments, about 15% better than ELWS.

Local‐area nanosecond‐accuracy time synchronisation based on GPS L1 observations

To date, high-precision time synchronisation has been applied in many fields, such as high-precision indoor positioning, multistatic Synthetic Aperture Radar (SAR) and etc. This study focuses on the use of only global positioning system (GPS) L1 civilian signals to achieve regional nanosecond time synchronisation. GPS errors are analysed in this study to determine their effect on GPS timing. This study also examines the specific features of the error in fixed-position timing technology and related error reduction techniques to illustrate the feasibility of nanosecond high-precision time synchronisation with GPS L1 signals in a fixed-position timing mode. Then, the results of a specific GPS timing receiver working in a fixed-position mode and using GPS L1 signals are shown to indicate that the use of GPS L1 signals to achieve nanosecond-accuracy time synchronisation in a local area is feasible. Finally, this study also analyses the impacts of the imprecise position in a fixed-position timing mode.

Spatial Video Health Risk Mapping in Informal Settlements: Correcting GPS Error.

Informal settlements pose a continuing health concern. While spatial methodologies have proven to be valuable tools to support health interventions, several factors limit their widespread use in these challenging environments. One such technology, spatial video, has been used for fine-scale contextualized mapping. In this paper, we address one of the limitations of the technique: the global positioning system (GPS) coordinate error. More specifically, we show how spatial video coordinate streams can be corrected and synced back to the original video to facilitate risk mapping. Past spatial video collections for the Mathare informal settlement of Kenya are used as an illustration as these data had been previously discarded because of excessive GPS error. This paper will describe the bespoke software that makes these corrections possible, and then will go on to investigate patterns in the coordinate error.

An Environmentally-Adaptive Positioning Method Based on Integration of GPS/DTMB/FM.

The Global Positioning System (GPS) yields good precision and availability in open outdoor environment. However, the errors of GPS may suffer degradation in some complex environments, such as forests and urban canyons. To solve this problem, a new positioning method is designed integrating GPS, Digital Terrestrial Multimedia Broadcast (DTMB) and frequency-modulated (FM) radio signal. In this method, the DTMB transmitter acts as a pseudo-satellite to assist GPS positioning. Furthermore, the FM fingerprint positioning is used to correct the positioning bias. An adaptive selection scheme is proposed to provide an optimal integration mode of the sensors. Field experiments in complex environment were carried out for evaluation. Comparing to the GPS-only and GPS + DTMB approach, positioning accuracy was improved by at least and with the proposed method, respectively.

Swift Path Planning: Vehicle Localization by Visual Odometry Trajectory Tracking and Mapping

Accurate localization is the key component in intelligent vehicles for navigation. With the rapid development especially in urban area, the increasing high-rise buildings results in urban canyon and road network has become more complex. These affect the vehicle navigation performance particularly in the event of poor Global Positioning System (GPS) signal. Therefore, it is essential to develop a perceptive localization system to overcome this problem. This paper proposes a localization approach that exhibits the advantages of Visual Odometry (VO) in low-cost data fusion to reduce vehicle localization error and improve its response rate in path selection. The data used are sourced from camera as visual sensor, low-cost GPS and free digital map from OpenStreetMap. These data are fused by Particle filter (PF) where our method estimates the curvature similarity score of VO trajectory curve with candidate ways extracted from the map. We evaluate the robustness of our proposed approach with three types of GPS errors such as random noise, biased noise and GPS signal loss in an instance of ambiguous road decision. Our results show that this method is able to detect and select the correct path simultaneously which contributes to a swift path planning.

An Accurate High-Order Errors Suppression and Cancellation Method for High-Precision Airborne POS

Synthetic aperture radar imaging requires precise position information provided by position and orientation system (POS), especially sensitive to high-order errors of position information, whereas high-order errors of global positioning system (GPS) are normally ignored in POS solution procedure. For high-precision POS, high-order errors become a significant error source with decisive effects on the accuracy of POS. In this paper, we propose a GPS high-order position errors suppression and cancellation method, which includes the traditional GPS/inertia navigation system (INS) integrated method used as the high-frequency filter, and the low-frequency filter is based on least squares support vector machine. The high- and low-frequency dual filters are constructed to suppress and eliminate high-order error. In verifying our dual-rate hybrid filtering method, POS flight experiments were conducted in November and October up to December in 2015, with results showing that the high-order position accuracy of high-precision POS (0.01°/h gyro drift) is better than 0.006 m, and the proposed method has a better performance compared with other methods.

Validation of electronic performance and tracking systems EPTS under field conditions.

The purpose of this study was to assess the measurement accuracy of the most commonly used tracking technologies in professional team sports (i.e., semi-automatic multiple-camera video technology (VID), radar-based local positioning system (LPS), and global positioning system (GPS)). The position, speed, acceleration and distance measures of each technology were compared against simultaneously recorded measures of a reference system (VICON motion capture system) and quantified by means of the root mean square error RMSE. Fourteen male soccer players (age: 17.4±0.4 years, height: 178.6±4.2 cm, body mass: 70.2±6.2 kg) playing for the U19 Bundesliga team FC Augsburg participated in the study. The test battery comprised a sport-specific course, shuttle runs, and small sided games on an outdoor soccer field. The validity of fundamental spatiotemporal tracking data differed significantly between all tested technologies. In particular, LPS showed higher validity for measuring an athlete’s position (23±7 cm) than both VID (56±16 cm) and GPS (96±49 cm). Considering errors of instantaneous speed measures, GPS (0.28±0.07 m⋅s-1) and LPS (0.25±0.06 m⋅s-1) achieved significantly lower error values than VID (0.41±0.08 m⋅s-1). Equivalent accuracy differences were found for instant acceleration values (GPS: 0.67±0.21 m⋅s-2, LPS: 0.68±0.14 m⋅s-2, VID: 0.91±0.19 m⋅s-2). During small-sided games, lowest deviations from reference measures have been found in the total distance category, with errors ranging from 2.2% (GPS) to 2.7% (VID) and 4.0% (LPS). All technologies had in common that the magnitude of the error increased as the speed of the tracking object increased. Especially in performance indicators that might have a high impact on practical decisions, such as distance covered with high speed, we found >40% deviations from the reference system for each of the technologies. Overall, our results revealed significant between-system differences in the validity of tracking data, implying that any comparison of results using different tracking technologies should be done with caution.

PRECISE POSITIONING OF UAVS – DEALING WITH CHALLENGING RTK-GPS MEASUREMENT CONDITIONS DURING AUTOMATED UAV FLIGHTS

Abstract. For some years now, UAVs (unmanned aerial vehicles) are commonly used for different mobile mapping applications, such as in the fields of surveying, mining or archeology. To improve the efficiency of these applications an automation of the flight as well as the processing of the collected data is currently aimed at. One precondition for an automated mapping with UAVs is that the georeferencing is performed directly with cm-accuracies or better. Usually, a cm-accurate direct positioning of UAVs is based on an onboard multi-sensor system, which consists of an RTK-capable (real-time kinematic) GPS (global positioning system) receiver and additional sensors (e.g. inertial sensors). In this case, the absolute positioning accuracy essentially depends on the local GPS measurement conditions. Especially during mobile mapping applications in urban areas, these conditions can be very challenging, due to a satellite shadowing, non-line-of sight receptions, signal diffraction or multipath effects. In this paper, two straightforward and easy to implement strategies will be described and analyzed, which improve the direct positioning accuracies for UAV-based mapping and surveying applications under challenging GPS measurement conditions. Based on a 3D model of the surrounding buildings and vegetation in the area of interest, a GPS geometry map is determined, which can be integrated in the flight planning process, to avoid GPS challenging environments as far as possible. If these challenging environments cannot be avoided, the GPS positioning solution is improved by using obstruction adaptive elevation masks, to mitigate systematic GPS errors in the RTK-GPS positioning. Simulations and results of field tests demonstrate the profit of both strategies.

Study of mobile measurements for detailed temperature distribution in a high-density urban area in Tokyo

Due to the low spatial resolution of measurements from meteorological observation stations, it is difficult to observe urban environmental parameters at a scale relevant to people's daily lives. Mobile measurements can be an effective method of investigating the distribution of environmental conditions in urban areas. However, the effectiveness of mobile measurements is unclear as it is difficult to obtain accurate positioning data for each sample due to Global Positioning System (GPS) errors that may be >20m in urban areas. We conducted a mobile measurement study during August 25th to 28th, 2015, in Shinjuku, Tokyo, one of the most crowded areas in Japan, to investigate the spatial distribution of air temperature and the range of GPS errors. We equipped bicycles with thermometers and high temporal resolution GPS loggers to measure air temperature and spatial position. The results demonstrated that the range of air temperature variation was almost 2°C on cloudy days, and that the spatial distribution of air temperature differed for different measurement routes. A spatial resolution in the order of 101m was required to detect locations where the air temperature was locally high, such as near street intersections.

Comparison and analysis of unmodelled errors in GPS and BeiDou signals

In Global Navigation Satellite Systems (GNSS) positioning, one often tries to establish a mathematic model to capture the systematic behaviors of observations as much as possible. However, the observation residuals still exhibit, to a great extent, as (somewhat systematic) signals. Nevertheless, those systematic variations are referred to as the unmodelled errors, which are difficult to be further modelled by setting up additional parameters. Different from the random errors, the unmodelled errors are colored and time correlated. In general, the larger the time correlations are, the more significant the unmodelled errors. Hence, understanding the time correlations of unmodelled errors is important to develop the theory for processing the unmodelled errors. In this study, we compare and analyze the time correlations caused by unmodelled errors of Global Positioning System (GPS) and BeiDou signals. The time correlations are estimated based on the residuals of double differenced observations on 11 baselines with different lengths. The results show that the time correlation patterns are different significantly between GPS and BeiDou observations. Besides, the code and phase data from the same satellite system are also not the same. Furthermore, the unmodelled errors are affected by not only the baseline length, but also some other factors. In addition, to make use of the time correlations with more efficiency, we propose to fit the time correlations by using exponent and quadratic models and the fitting coefficients are given. Finally, the sequential adjustment method considering the time correlations is implemented to compute the baseline solutions. The results show that the solutions considering the time correlations can objectively reflect the actual precisions of parameter estimates.