scholarly journals NLOS Satellite Detection Using Fish-Eye Camera for Improving GNSS Positioning Accuracy ― Further Results

2020 ◽  
Vol 2020 (0) ◽  
pp. 54-59
Author(s):  
Akihiro Yoshida ◽  
Yukihiro Kubo ◽  
Yoshiraru Koya
Keyword(s):  
Positioning Accuracy ◽  
Gnss Positioning ◽  
Fish Eye

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 Helmert Variance Component Estimation for Multi-GNSS Relative Positioning

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 669 ◽  
Author(s):  
Mowen Li ◽  
Wenfeng Nie ◽  
Tianhe Xu ◽  
Adria Rovira-Garcia ◽  
Zhenlong Fang ◽  
...  
Keyword(s):  
Variance Component ◽  
Computation Time ◽  
Satellite System ◽  
Unit Weight ◽  
Variance Component Estimation ◽  
Positioning Accuracy ◽  
Gnss Positioning ◽  
Component Estimation ◽  
Weighting Strategy ◽  
Gnss Constellation

The Multi-constellation Global Navigation Satellite System (Multi-GNSS) has become the standard implementation of high accuracy positioning and navigation applications. It is well known that the noise of code and phase measurements depend on GNSS constellation. Then, Helmert variance component estimation (HVCE) is usually used to adjust the contributions of different GNSS constellations by determining their individual variances of unit weight. However, HVCE requires a heavy computation load. In this study, the HVCE posterior weighting was employed to carry out a kinematic relative Multi-GNSS positioning experiment with six short-baselines from day of year (DoY) 171 to 200 in 2019. As a result, the HVCE posterior weighting strategy improved Multi-GNSS positioning accuracy by 20.5%, 15.7% and 13.2% in east-north-up (ENU) components, compared to an elevation-dependent (ED) priori weighting strategy. We observed that the weight proportion of both code and phase observations for each GNSS constellation were consistent during the entire 30 days, which indicates that the weight proportions of both code and phase observations are stable over a long period of time. It was also found that the quality of a phase observation is almost equivalent in each baseline and GNSS constellation, whereas that of a code observation is different. In order to reduce the time consumption of the HVCE method without sacrificing positioning accuracy, the stable variances of unit weights of both phase and code observations obtained over 30 days were averaged and then frozen as a priori information in the positioning experiment. The result demonstrated similar ENU improvements of 20.0%, 14.1% and 11.1% with respect to the ED method but saving 88% of the computation time of the HCVE strategy. Our study concludes with the observations that the frozen variances of unit weight (FVUW) could be applied to the positioning experiment for the next 30 days, that is, from DoY 201 to 230 in 2019, improving the positioning ENU accuracy of the ED method by 18.1%, 13.2% and 10.6%, indicating the effectiveness of the FVUW.

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.

scholarly journals Smartphone Positioning and Accuracy Analysis Based on Real-Time Regional Ionospheric Correction Model

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3879
Author(s):  
Qi Liu ◽  
Chengfa Gao ◽  
Zihan Peng ◽  
Ruicheng Zhang ◽  
Rui Shang
Keyword(s):  
Real Time ◽  
Convergence Time ◽  
Electron Content ◽  
Ionospheric Model ◽  
Positioning Accuracy ◽  
Correction Model ◽  
The Real ◽  
Gnss Positioning ◽  
Klobuchar Model ◽  
Regional Ionospheric Model

As one of the main errors that affects Global Navigation Satellite System (GNSS) positioning accuracy, ionospheric delay also affects the improvement of smartphone positioning accuracy. The current ionospheric error correction model used in smartphones has a certain time delay and low accuracy, which is difficult to meet the needs of real-time positioning of smartphones. This article proposes a method to use the real-time regional ionospheric model retrieved from the regional Continuously Operating Reference Stations (CORS) observation data to correct the GNSS positioning error of the smartphone. To verify the accuracy of the model, using the posterior grid as the standard, the electron content error of the regional ionospheric model is less than 5 Total Electron Content Unit (TECU), which is about 50% higher than the Klobuchar model, and to further evaluate the impact of the regional ionosphere model on the real-time positioning accuracy of smartphones, carrier-smoothing pseudorange and single-frequency Precise Point Positioning (PPP) tests were carried out. The results show that the real-time regional ionospheric model can significantly improve the positioning accuracy of smartphones, especially in the elevation direction. Compared with the Klobuchar model, the improvement effect is more than 34%, and the real-time regional ionospheric model also shortens the convergence time of the elevation direction to 1 min. (The convergence condition is that the range of continuous 20 s is less than 0.5 m).

Vehicle Localization Based on Global Navigation Satellite System Aided by Three-Dimensional Map

2017 ◽  
Vol 2621 (1) ◽  
pp. 55-61 ◽  
Author(s):  
Yanlei Gu ◽  
Li-Ta Hsu ◽  
Shunsuke Kamijo
Keyword(s):  
Inertial Sensors ◽  
Three Dimensional ◽  
Satellite System ◽  
Navigation Satellite ◽  
Positioning Accuracy ◽  
Vehicle Localization ◽  
Localization System ◽  
Gnss Positioning ◽  
Positioning Method ◽  
Global Navigation Satellite

Accurate vehicle localization technologies are significant for current onboard navigation systems and future autonomous vehicles. More specifically, positioning accuracy is expected at the submeter level. This paper presents an accurate vehicle self-localization system and evaluates the proposed system in different classes of urban environments. The developed system adopts an innovative global navigation satellite system (GNSS) positioning method as the key technique. The GNSS positioning method can improve the positioning error by reducing the effects of multipath interference and non-line-of-sight errors with the aid of a three-dimensional map. To improve positioning accuracy further, the vehicle localization system integrates the GNSS positioning technique with inertial sensors and vision sensors by considering the characteristics of each sensor. The inertial sensors represent vehicle movement with heading direction and vehicle speed. The vision sensor is used to recognize the position change relative to lane markings on the road surface. Those techniques and sensors collaborate to provide an accurate position in the global coordinate system. To verify the effectiveness and stability of the proposed system, a series of tests was conducted in one of the most challenging urban cities, Tokyo. The experiment results demonstrate that the proposed system can achieve submeter accuracy for the positioning error mean and has a 90% correct lane rate in the localization.

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