Integrity monitoring of vehicle positioning in urban environment using RTK-GNSS, IMU and speedometer

El‐Mowafy, Ahmed; Kubo, Nobuaki

doi:10.1088/1361-6501/aa5c66

Cited by 43 publications

(50 citation statements)

References 20 publications

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“…Normally, a velocity pulse generation device counts the number of pulses per rotation of the wheel. In this study, we use the speed pulse obtained from the POSLVX system, which is a wheel-mounted rotary shaft encoder that accurately measures the linear distance covered by the vehicle [25]. The two methods, Doppler calibrated IMU and Odometer+IMU, can only estimate temporal position changes, and hence, their positioning errors accumulate with time, in particular, the heading bias of the IMU.…”

Section: Simple Integration Of Low-cost Gnss Imu and Odometermentioning

confidence: 99%

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Reliable Positioning and Journey Planning for Intelligent Transport Systems

El‐Mowafy¹,

Kubo²,

Kealy³

2020

Intelligent and Efficient Transport Systems - Design, Modelling, Control and Simulation

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Safety and reliability of intelligent transport systems applications require positioning accuracy at the sub-meter level with availability and integrity above 99%. At present, no single positioning sensor can meet these requirements in particular in the urban environment. Possible sensors that can be used for this task are first reviewed. Next, a suggested integrated system of low-cost real-time kinematic (RTK) GNSS, inertial measurement units (IMU) and vehicle odometer is discussed. To ensure positioning integrity, a method for fault detection in GNSS observations and computation of the protection levels (PL) that bound the position errors at a pre-set risk probability of the integrated sensors are presented. A case study is performed for demonstration. Moreover, to save energy, reduce pollution, and to improve the economy of the trip, proper journey planning is required. A new approach is introduced using 3D city models to predict the route with the best positioning integrity, availability and precision for route selection among different possible routes. The practical demonstration shows that effectiveness of this method. Finally, the potential of using the next generation SBAS for ITS applications was tested using kinematic tests carried out in various environments characterized by different levels of sky-visibility that may affect observations from GNSS.

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Section: Simple Integration Of Low-cost Gnss Imu and Odometermentioning

confidence: 99%

“…These velocities are integrated in time to provide the time changes in position in Easting and Northing directions. The observations are considered in this case as the mean values of the IMU heading (θ) and the odometer speed V ss , for instance between the epochs t-1 and t. Thus, the G matrix is expressed as [25]:…”

Section: Fault Detectionmentioning

confidence: 99%

Reliable Positioning and Journey Planning for Intelligent Transport Systems

El‐Mowafy¹,

Kubo²,

Kealy³

2020

Intelligent and Efficient Transport Systems - Design, Modelling, Control and Simulation

Self Cite

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“…36,37 The GPS errors were generated by a zero mean Gaussian distribution with 15 m variance. [44][45][46][47][48] The C-SOM is inexpensive and easy implement because it just relies on the GPS fixes and the information exchange between neighboring vehicles. 41,42 Considering factors such as safety requirement and lane change, the accuracy and speed of vehicle positioning method in the automatic driving system are high.…”

Section: Simulation Settingsmentioning

confidence: 99%

“…In addition, the global navigation satellite system combines with IMU or RTK and the LiDAR-based localization methods can achieve centimeter-level positioning accuracy. [44][45][46][47][48] The C-SOM is inexpensive and easy implement because it just relies on the GPS fixes and the information exchange between neighboring vehicles.…”

Section: Simulation Settingsmentioning

confidence: 99%

Improving positioning accuracy based on self‐organizing map (SOM) and inter‐vehicular communication

Lee

2019

Trans Emerging Tel Tech

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Low‐cost global positioning system (GPS) receivers installed in most vehicles and smart devices typically display tens of meters of error in their location data. A large amount of research effort has been made to increase the positioning accuracy of GPS receivers mostly by reducing errors with the help of auxiliary devices and/or methodologies such as differential GPSs, assisted GPSs, real‐time kinematic positioning, computer vision, etc, which result in a certain amount of cost increase. In this paper, we propose a new cooperative vehicular localization scheme based on vehicle‐to‐vehicle communication. The proposed scheme first estimates the distances between neighboring vehicles using the weighted least square of double difference scheme and uses a novel machine learning technique called constrained self‐organizing map (C‐SOM) with a set of adjusted GPS fixes to generate the final estimates of GPS locations with considerably lower errors. We also propose a new method of presenting training samples to address the issue of convergence failure that arises from the constraints imposed on the inter‐vehicular distances. We present simulation results that demonstrate the superior performance of the proposed scheme over both the conventional SOM and the distributed location estimate algorithm.

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“…Reference [8] presents a comprehensive literature review of current progress in the application of state-of-the-art indoor positioning systems for telecare and telehealth monitoring. Reference [9] presents an integrity monitoring methodology for a proposed system, which includes Real-time kinematic Global Navigation Satellite System integrated with low-cost Inertial measurement unit and automotive speedometer to ensure trustworthy precise positioning for advanced vehicle positioning. A new underground localization algorithm using a re-measurement of the sequence of the magnetic field and pose graph simultaneous localization and mapping for directional drilling is proposed in [10].…”

Section: Introductionmentioning

confidence: 99%

Ultra-wideband pose detection system for boom-type roadheader based on Caffery transform and Taylor series expansion

Zhang

et al. 2017

Meas. Sci. Technol.

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To realize unmanned pose detection of a coalmine boom-type roadheader, an ultra-wideband (UWB) pose detection system (UPDS) for a roadheader is designed, which consists of four UWB positioning base stations and three roadheader positioning nodes. The positioning base stations are used in turn to locate the positioning nodes of the roadheader fuselage. Using 12 sets of distance measurement information, a time-of-arrival (TOA) positioning model is established to calculate the 3D coordinates of three positioning nodes of the roadheader fuselage, and the three attitude angles (heading, pitch, and roll angles) of the roadheader fuselage are solved. A range accuracy experiment of a UWB P440 module was carried out in a narrow and closed tunnel, and the experiment data show that the mean error and standard deviation of the module can reach below 2 cm. Based on the TOA positioning model of the UPDS, we propose a fusion-positioning algorithm based on a Caffery transform and Taylor series expansion (CTFPA). We derived the complete calculation process, designed a flowchart, and carried out a simulation of CTFPA in MATLAB, comparing 1000 simulated positioning nodes of CTFPA and the Caffery positioning algorithm (CPA) for a 95 m long tunnel. The positioning error field of the tunnel was established, and the influence of the spatial variation on the positioning accuracy of CPA and CTFPA was analysed. The simulation results show that, compared with CPA, the positioning accuracy of CTFPA is clearly improved, and the accuracy of each axis can reach more than 5 mm. The accuracy of the X-axis is higher than that of the Y-and Z-axes. In section X-Y of the tunnel, the root mean square error (RMSE) contours of CTFPA are clear and orderly, and with an increase in the measuring distance, RMSE increases linearly. In section X-Z, the RMSE contours are concentric circles, and the variation ratio is nonlinear.

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Integrity monitoring of vehicle positioning in urban environment using RTK-GNSS, IMU and speedometer

Cited by 43 publications

References 20 publications

Reliable Positioning and Journey Planning for Intelligent Transport Systems

Reliable Positioning and Journey Planning for Intelligent Transport Systems

Improving positioning accuracy based on self‐organizing map (SOM) and inter‐vehicular communication

Ultra-wideband pose detection system for boom-type roadheader based on Caffery transform and Taylor series expansion

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