NLOS Satellite Detection Using a Fish-Eye Camera for Improving GNSS Positioning Accuracy in Urban Area

2016 ◽  
Vol 28 (1) ◽  
pp. 31-39 ◽  
Author(s):  
Shodai Kato ◽  
◽  
Mitsunori Kitamura ◽  
Taro Suzuki ◽  
Yoshiharu Amano ◽  
...  
Keyword(s):  
Weather Conditions ◽  
Urban Environments ◽  
Global Navigation Satellite Systems ◽  
Image Feature ◽  
Transport Systems ◽  
Positioning Accuracy ◽  
Positioning Systems ◽  
Satellite Systems ◽  
Gnss Positioning ◽  
Fish Eye

[abstFig src='/00280001/03.jpg' width=""300"" text='NLOS satellites detection method' ]In recent years, global navigation satellite systems (GNSSs) have been widely used in intelligent transport systems (ITSs), and many countries have been rapidly improving the infrastructure of their satellite positioning systems. However, there is a serious problem involving the use of kinematic GNSS positioning in urban environments, which stems from significant positioning errors introduced by non-line-of-sight (NLOS) satellites blocked by obstacles. Therefore, we propose the method for positioning based on NLOS satellites detection using a fish-eye camera. In general, it is difficult to robustly extract an obstacle region from the fish-eye image because the image is affected by cloud cover, illumination conditions, and weather conditions. We extract the obstacle region from the image by tracking image feature points in sequential images. Because the obstacle region on the image moves larger than the sky region, the obstacle region can be determined by performing image segmentation and by using feature point tracking techniques. Finally, NLOS satellites can be identified using the obstacle region on the image. The evaluation results confirm the GNSS positioning accuracy without the NLOS satellites was improved compared with using all observed satellites, and confirm the effectiveness of the proposed technique and the feasibility of implementing its highly accurate positioning capabilities in urban environments.

scholarly journals Road Tests of the Positioning Accuracy of INS/GNSS Systems Based on MEMS Technology for Navigating Railway Vehicles

Energies ◽  
2020 ◽  
Vol 13 (17) ◽  
pp. 4463 ◽  
Author(s):  
Mariusz Specht ◽  
Cezary Specht ◽  
Paweł Dąbrowski ◽  
Krzysztof Czaplewski ◽  
Leszek Smolarek ◽  
...  
Keyword(s):  
Urban Areas ◽  
Land Development ◽  
Global Navigation Satellite Systems ◽  
Rolling Stock ◽  
Navigation Systems ◽  
Positioning Accuracy ◽  
Positioning Systems ◽  
Satellite Systems ◽  
Wide Range ◽  
Mems Technology

Thanks to the support of Inertial Navigation Systems (INS), Global Navigation Satellite Systems (GNSS) provide a navigation positioning solution that, in the absence of satellite signals (in tunnels, forest and urban areas), allows the continuous positioning of a moving object (air, land and sea). Passenger and freight trains must, for safety reasons, comply with several formal navigation requirements, particularly those that concern the minimum acceptable accuracy for determining their position. Depending on the type of task performed by the train (positioning a vehicle on a route, stopping at a turnout, stopping at a platform, monitoring the movement of rolling stock, etc.), the train must have positioning systems that can determine its position with sufficient accuracy (1–10 m, p = 0.95) to perform the tasks in question. A wide range of INS/GNSS equipment is currently available, ranging from very costly to simple solutions based on Micro-Electro-Mechanical Systems (MEMS), which, in addition to an inertial unit, use one or two GNSS receivers. The paper presents an assessment of the accuracy of both types of solutions by testing them simultaneously in dynamic measurements. The research, due to the costs and logistics complexity, was made using a passenger car. The surveys were carried out in a complex way, because the measurement route was travelled three times at four different speeds: 40 km/h, 80 km/h, 100 km/h and 120 km/h on seven representative test sections with diverse land development. In order to determine the positioning accuracy of INS devices, two precise GNSS geodetic receivers (2 cm accuracy, p = 0.95) were used as a reference positioning system. The measurements demonstrated that only INS/GNSS systems based on two receivers can meet the requirements of most railway applications related to rail navigation, and since a solution with a single GNSS receiver has a much lower positioning accuracy, it is not suitable for many railway applications. It is noted that considerable differences between the standards defining the navigation requirements for railway applications. For example, INS/GNSS systems based on two receivers meet the vast majority of the expectations specified in the Report on Rail User Needs and Requirements. However, according to the Federal Radionavigation Plan (FRP), it cannot be used in any railway application.

scholarly journals Robust TOA-Based UAS Navigation under Model Mismatch in GNSS-Denied Harsh Environments

2020 ◽  
Vol 12 (18) ◽  
pp. 2928
Author(s):  
Jan Mortier ◽  
Gaël Pagès ◽  
Jordi Vilà-Valls
Keyword(s):  
Intelligent Transportation Systems ◽  
Autonomous Systems ◽  
Real Life ◽  
Transportation Systems ◽  
Urban Environments ◽  
Global Navigation Satellite Systems ◽  
Ultra Wideband ◽  
Time Of Arrival ◽  
Navigation Systems ◽  
Satellite Systems

Global Navigation Satellite Systems (GNSS) is the technology of choice for outdoor positioning purposes but has many limitations when used in safety-critical applications such Intelligent Transportation Systems (ITS) and Unmanned Autonomous Systems (UAS). Namely, its performance clearly degrades in harsh propagation conditions and is not reliable due to possible attacks or interference. Moreover, GNSS signals may not be available in the so-called GNSS-denied environments, such as deep urban canyons or indoors, and standard GNSS architectures do not provide the precision needed in ITS. Among the different alternatives, cellular signals (LTE/5G) may provide coverage in constrained urban environments and Ultra-Wideband (UWB) ranging is a promising solution to achieve high positioning accuracy. The key points impacting any time-of-arrival (TOA)-based navigation system are (i) the transmitters’ geometry, (ii) a perfectly known transmitters’ position, and (iii) the environment. In this contribution, we analyze the performance loss of alternative TOA-based navigation systems in real-life applications where we may have both transmitters’ position mismatch, harsh propagation environments, and GNSS-denied conditions. In addition, we propose new robust filtering methods able to cope with both effects up to a certain extent. Illustrative results in realistic scenarios are provided to support the discussion and show the performance improvement brought by the new methodologies with respect to the state-of-the-art.

scholarly journals Practical Modeling of GNSS for Autonomous Vehicles in Urban Environments

Sensors ◽  
2019 ◽  
Vol 19 (19) ◽  
pp. 4236
Author(s):  
Woosik Lee ◽  
Hyojoo Cho ◽  
Seungho Hyeong ◽  
Woojin Chung
Keyword(s):  
Motion Estimation ◽  
Autonomous Vehicles ◽  
Autonomous Navigation ◽  
Urban Environments ◽  
Global Navigation Satellite Systems ◽  
Navigation Satellite ◽  
Key Technology ◽  
Satellite Systems ◽  
Agricultural Robots ◽  
Global Navigation Satellite

Autonomous navigation technology is used in various applications, such as agricultural robots and autonomous vehicles. The key technology for autonomous navigation is ego-motion estimation, which uses various sensors. Wheel encoders and global navigation satellite systems (GNSSs) are widely used in localization for autonomous vehicles, and there are a few quantitative strategies for handling the information obtained through their sensors. In many cases, the modeling of uncertainty and sensor fusion depends on the experience of the researchers. In this study, we address the problem of quantitatively modeling uncertainty in the accumulated GNSS and in wheel encoder data accumulated in anonymous urban environments, collected using vehicles. We also address the problem of utilizing that data in ego-motion estimation. There are seven factors that determine the magnitude of the uncertainty of a GNSS sensor. Because it is impossible to measure each of these factors, in this study, the uncertainty of the GNSS sensor is expressed through three variables, and the exact uncertainty is calculated. Using the proposed method, the uncertainty of the sensor is quantitatively modeled and robust localization is performed in a real environment. The approach is validated through experiments in urban environments.

scholarly journals Seamless Vehicle Positioning by Lidar-GNSS Integration: Standalone and Multi-Epoch Scenarios

2021 ◽  
Vol 13 (22) ◽  
pp. 4525
Author(s):  
Junjie Zhang ◽  
Kourosh Khoshelham ◽  
Amir Khodabandeh
Keyword(s):  
Autonomous Driving ◽  
Measurement Model ◽  
Urban Environments ◽  
Global Navigation Satellite Systems ◽  
High Definition ◽  
Satellite Systems ◽  
The Road ◽  
Gnss Receivers ◽  
Vehicle Positioning ◽  
Global Navigation Satellite

Accurate and seamless vehicle positioning is fundamental for autonomous driving tasks in urban environments, requiring the provision of high-end measuring devices. Light Detection and Ranging (lidar) sensors, together with Global Navigation Satellite Systems (GNSS) receivers, are therefore commonly found onboard modern vehicles. In this paper, we propose an integration of lidar and GNSS code measurements at the observation level via a mixed measurement model. An Extended Kalman-Filter (EKF) is implemented to capture the dynamic of the vehicle movement, and thus, to incorporate the vehicle velocity parameters into the measurement model. The lidar positioning component is realized using point cloud registration through a deep neural network, which is aided by a high definition (HD) map comprising accurately georeferenced scans of the road environments. Experiments conducted in a densely built-up environment show that, by exploiting the abundant measurements of GNSS and high accuracy of lidar, the proposed vehicle positioning approach can maintain centimeter-to meter-level accuracy for the entirety of the driving duration in urban canyons.

scholarly journals A New Asynchronous RTK Method to Mitigate Base Station Observation Outages

Sensors ◽  
2019 ◽  
Vol 19 (15) ◽  
pp. 3376 ◽  
Author(s):  
Yuan Du ◽  
Guanwen Huang ◽  
Qin Zhang ◽  
Yang Gao ◽  
Yuting Gao
Keyword(s):  
Real Time ◽  
Base Station ◽  
Global Navigation Satellite Systems ◽  
Observation Data ◽  
Post Processing ◽  
Positioning Accuracy ◽  
Real Time Processing ◽  
Phase Measurements ◽  
Satellite Systems ◽  
Precise Ephemeris

Real-time kinematic (RTK) positioning is a satellite navigation technique that is widely used to enhance the precision of position data obtained from global navigation satellite systems (GNSS). This technique can reduce or eliminate significant correlation errors via the enhancement of the base station observation data. However, observations received by the base station are often interrupted, delayed, and/or discontinuous, and in the absence of base station observation data the corresponding positioning accuracy of a rover declines rapidly. With the strategies proposed till date, the positioning accuracy can only be maintained at the centimeter-level for a short span of time, no more than three min. To address this, a novel asynchronous RTK method (that addresses asynchronous errors) that can bridge significant gaps in the observations at the base station is proposed. First, satellite clock and orbital errors are eliminated using the products of the final precise ephemeris during post-processing or the ultra-rapid precise ephemeris during real-time processing. Then the tropospheric error is corrected using the Saastamoinen model and the asynchronous ionospheric delay is corrected using the carrier phase measurements from the rover receiver. Finally, a straightforward first-degree polynomial function is used to predict the residual asynchronous error. Experimental results demonstrate that the proposed approach can achieve centimeter-level accuracy for as long as 15 min during interruptions in both real-time and post-processing scenarios, and that the accuracy of the real-time scheme can be maintained for 15 min even when a large systematic error is projected in the U direction.

scholarly journals GNSS Performance Modelling and Augmentation for Urban Air Mobility

Sensors ◽  
2019 ◽  
Vol 19 (19) ◽  
pp. 4209 ◽  
Author(s):  
Suraj Bijjahalli ◽  
Roberto Sabatini ◽  
Alessandro Gardi
Keyword(s):  
Failure Modes ◽  
Primary Source ◽  
Urban Environments ◽  
Unmanned Aircraft ◽  
Global Navigation Satellite Systems ◽  
Urban Air ◽  
Performance Modelling ◽  
Satellite Systems ◽  
Urban Structures ◽  
Global Navigation Satellite

One of the primary challenges facing Urban Air Mobility (UAM) and the safe integration of Unmanned Aircraft Systems (UAS) in the urban airspace is the availability of robust, reliable navigation and Sense-and-Avoid (SAA) systems. Global Navigation Satellite Systems (GNSS) are typically the primary source of positioning for most air and ground vehicles and for a growing number of UAS applications; however, their performance is frequently inadequate in such challenging environments. This paper performs a comprehensive analysis of GNSS performance for UAS operations with a focus on failure modes in urban environments. Based on the analysis, a guidance strategy is developed which accounts for the influence of urban structures on GNSS performance. A simulation case study representative of UAS operations in urban environments is conducted to assess the validity of the proposed approach. Results show improved accuracy (approximately 25%) and availability when compared against a conventional minimum-distance guidance strategy.

scholarly journals Robust Statistics for GNSS Positioning under Harsh Conditions: A Useful Tool?

Sensors ◽  
2019 ◽  
Vol 19 (24) ◽  
pp. 5402 ◽  
Author(s):  
Daniel Medina ◽  
Haoqing Li ◽  
Jordi Vilà-Valls ◽  
Pau Closas
Keyword(s):  
Robust Regression ◽  
Robust Statistics ◽  
Single Point ◽  
Global Navigation Satellite Systems ◽  
Noise Model ◽  
Satellite Systems ◽  
Gnss Positioning ◽  
Global Navigation Satellite ◽  
Harsh Conditions ◽  
Real World Problems

Navigation problems are generally solved applying least-squares (LS) adjustments. Techniques based on LS can be shown to perform optimally when the system noise is Gaussian distributed and the parametric model is accurately known. Unfortunately, real world problems usually contain unexpectedly large errors, so-called outliers, that violate the noise model assumption, leading to a spoiled solution estimation. In this work, the framework of robust statistics is explored to provide robust solutions to the global navigation satellite systems (GNSS) single point positioning (SPP) problem. Considering that GNSS observables may be contaminated by erroneous measurements, we survey the most popular approaches for robust regression (M-, S-, and MM-estimators) and how they can be adapted into a general methodology for robust GNSS positioning. We provide both theoretical insights and validation over experimental datasets, which serves in discussing the robust methods in detail.

scholarly journals On the Recursive Joint Position and Attitude Determination in Multi-Antenna GNSS Platforms

2020 ◽  
Vol 12 (12) ◽  
pp. 1955 ◽  
Author(s):  
Daniel Medina ◽  
Jordi Vilà-Valls ◽  
Anja Hesselbarth ◽  
Ralf Ziebold ◽  
Jesús García
Keyword(s):  
Attitude Determination ◽  
Base Station ◽  
Joint Estimation ◽  
Global Navigation Satellite Systems ◽  
Transport Systems ◽  
Satellite Systems ◽  
Dynamic Scenario ◽  
Correlated Information ◽  
New Formulation ◽  
Global Navigation Satellite

Global Navigation Satellite Systems’ (GNSS) carrier phase observations are fundamental in the provision of precise navigation for modern applications in intelligent transport systems. Differential precise positioning requires the use of a base station nearby the vehicle location, while attitude determination requires the vehicle to be equipped with a setup of multiple GNSS antennas. In the GNSS context, positioning and attitude determination have been traditionally tackled in a separate manner, thus losing valuable correlated information, and for the latter only in batch form. The main goal of this contribution is to shed some light on the recursive joint estimation of position and attitude in multi-antenna GNSS platforms. We propose a new formulation for the joint positioning and attitude (JPA) determination using quaternion rotations. A Bayesian recursive formulation for JPA is proposed, for which we derive a Kalman filter-like solution. To support the discussion and assess the performance of the new JPA, the proposed methodology is compared to standard approaches with actual data collected from a dynamic scenario under the influence of severe multipath effects.

scholarly journals Theoretical Upper Bound and Lower Bound for Integer Aperture Estimation Fail-Rate and Practical Implications

2012 ◽  
Vol 66 (3) ◽  
pp. 321-333 ◽  
Author(s):  
Tao Li ◽  
Jinling Wang
Keyword(s):  
Lower Bound ◽  
Upper Bound ◽  
Global Navigation Satellite Systems ◽  
Integer Ambiguity ◽  
Satellite Systems ◽  
Gnss Positioning ◽  
Integer Aperture Estimation ◽  
Ambiguity Validation ◽  
Global Navigation Satellite ◽  
Practical Implications

Integer ambiguity validation is pivotal in precise positioning with Global Navigation Satellite Systems (GNSS). Recent research has shown traditionally used ambiguity validation methods can be classified as members of the Integer Aperture (IA) estimators, and by the virtue of the IA estimation, a user controllable IA fail-rate is preferred. However, an appropriately chosen fail-rate is essential for ambiguity validation. In this paper, the upper bound and the lower bound for the IA fail-rate, which are extremely useful even at the designing stage of a GNSS positioning system, have been analysed, and numerical results imply that a meaningful IA fail-rate should be within this range.

scholarly journals An SVM Based Weight Scheme for Improving Kinematic GNSS Positioning Accuracy with Low-Cost GNSS Receiver in Urban Environments

Sensors ◽  
2020 ◽  
Vol 20 (24) ◽  
pp. 7265
Author(s):  
Zhitao Lyu ◽  
Yang Gao
Keyword(s):  
Real Time ◽  
Low Cost ◽  
Urban Environments ◽  
Least Square ◽  
Single Frequency ◽  
Line Of Sight ◽  
Support Vector ◽  
Positioning Accuracy ◽  
Gnss Receiver ◽  
Gnss Positioning

High-precision positioning with low-cost global navigation satellite systems (GNSS) in urban environments remains a significant challenge due to the significant multipath effects, non-line-of-sight (NLOS) errors, as well as poor satellite visibility and geometry. A GNSS system is typically implemented with a least-square (LS) or a Kalman-filter (KF) estimator, and a proper weight scheme is vital for achieving reliable navigation solutions. The traditional weight schemes are based on the signal-in-space ranging errors (SISRE), elevation and C/N0 values, which would be less effective in urban environments since the observation quality cannot be fully manifested by those values. In this paper, we propose a new multi-feature support vector machine (SVM) signal classifier-based weight scheme for GNSS measurements to improve the kinematic GNSS positioning accuracy in urban environments. The proposed new weight scheme is based on the identification of important features in GNSS data in urban environments and intelligent classification of line-of-sight (LOS) and NLOS signals. To validate the performance of the newly proposed weight scheme, we have implemented it into a real-time single-frequency precise point positioning (SFPPP) system. The dynamic vehicle-based tests with a low-cost single-frequency u-blox M8T GNSS receiver demonstrate that the positioning accuracy using the new weight scheme outperforms the traditional C/N0 based weight model by 65.4% and 85.0% in the horizontal and up direction, and most position error spikes at overcrossing and short tunnels can be eliminated by the new weight scheme compared to the traditional method. It also surpasses the built-in satellite-based augmentation systems (SBAS) solutions of the u-blox M8T and is even better than the built-in real-time-kinematic (RTK) solutions of multi-frequency receivers like the u-blox F9P and Trimble BD982.

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