Filtering Link Outliers in Vehicle Trajectories by Spatial Reasoning

Vehicle trajectories derived from Global Navigation Satellite Systems (GNSS) are used in various traffic applications based on trajectory quality analysis for the development of successful traffic models. A trajectory consists of points and links that are connected, where both the points and links are subject to positioning errors in the GNSS. Existing trajectory filters focus on point outliers, but neglect link outliers on tracks caused by a long sampling interval. In this study, four categories of link outliers are defined, i.e., radial, drift, clustered, and shortcut; current available algorithms are applied to filter apparent point outliers for the first three categories, and a novel filtering approach is proposed for link outliers of the fourth category in urban areas using spatial reasoning rules without ancillary data. The proposed approach first measures specific geometric properties of links from trajectory databases and then evaluates the similarities of geometric measures among the links, following a set of spatial reasoning rules to determine link outliers. We tested this approach using taxi trajectory datasets for Beijing with a built-in sampling interval of 50 to 65 s. The results show that clustered links (27.14%) account for the majority of link outliers, followed by shortcut (6.53%), radial (3.91%), and drift (0.62%) outliers.

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Integrity and Collaboration in Dynamic Sensor Networks

Sensors ◽

10.3390/s18072400 ◽

2018 ◽

Vol 18 (7) ◽

pp. 2400 ◽

Cited By ~ 7

Author(s):

Steffen Schön ◽

Claus Brenner ◽

Hamza Alkhatib ◽

Max Coenen ◽

Hani Dbouk ◽

...

Keyword(s):

Sensor Networks ◽

Urban Areas ◽

Research Training ◽

Training Group ◽

Autonomous Driving ◽

Global Navigation Satellite Systems ◽

Satellite Systems ◽

Global Navigation Satellite ◽

Map Data ◽

Dynamic Sensor Networks

Global Navigation Satellite Systems (GNSS) deliver absolute position and velocity, as well as time information (P, V, T). However, in urban areas, the GNSS navigation performance is restricted due to signal obstructions and multipath. This is especially true for applications dealing with highly automatic or even autonomous driving. Subsequently, multi-sensor platforms including laser scanners and cameras, as well as map data are used to enhance the navigation performance, namely in accuracy, integrity, continuity and availability. Although well-established procedures for integrity monitoring exist for aircraft navigation, for sensors and fusion algorithms used in automotive navigation, these concepts are still lacking. The research training group i.c.sens, integrity and collaboration in dynamic sensor networks, aims to fill this gap and to contribute to relevant topics. This includes the definition of alternative integrity concepts for space and time based on set theory and interval mathematics, establishing new types of maps that report on the trustworthiness of the represented information, as well as taking advantage of collaboration by improved filters incorporating person and object tracking. In this paper, we describe our approach and summarize the preliminary results.

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Precise Point Positioning using Multi-Constellation GNSS Observations for Kinematic Applications

Journal of Applied Geodesy ◽

10.1515/jag-2014-0021 ◽

2015 ◽

Vol 9 (1) ◽

Cited By ~ 12

Author(s):

Mahmoud Abd Rabbou ◽

Ahmed El-Rabbany

Keyword(s):

Urban Areas ◽

Precise Point Positioning ◽

Global Navigation Satellite Systems ◽

Test Scenario ◽

Satellite Systems ◽

Satellite Geometry ◽

Point Positioning ◽

Kinematic Ppp ◽

Gnss Measurements ◽

Global Navigation Satellite

AbstractTraditional precise point positioning (PPP) is commonly based on un-differenced ionosphere-free linear combination of Global Positioning System (GPS) observations. Unfortunately, for kinematic applications, GPS often experiences poor satellite visibility or weak satellite geometry in urban areas. To overcome this limitation, we developed a PPP model, which combines the observations of three global navigation satellite systems (GNSS), namely GPS, GLONASS and Galileo. Both un-differenced and between-satellite single-difference (BSSD) ionosphere-free linear combinations of pseudorange and carrier phase GNSS measurements are processed. The performance of the combined GNSS PPP solution is compared with the GPS-only PPP solution using a real test scenario in downtown Kingston, Ontario. Inter-system biases between GPS and the other two systems are also studied and obtained as a byproduct of the PPP solution. It is shown that the addition of GLONASS observations improves the kinematic PPP solution accuracy in comparison with that of GPS-only solution. However, the contribution of adding Galileo observations is not significant due to the limited number of Galileo satellites launched up to date. In addition, BSSD solution is found to be superior to that of traditional un-differenced model.

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FULLY AUTOMATIC FEATURE-BASED REGISTRATION OF MOBILE MAPPING AND AERIAL NADIR IMAGES FOR ENABLING THE ADJUSTMENT OF MOBILE PLATFORM LOCATIONS IN GNSS-DENIED URBAN ENVIRONMENTS

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-1-w1-317-2017 ◽

2017 ◽

Vol XLII-1/W1 ◽

pp. 317-323 ◽

Cited By ~ 2

Author(s):

P. Jende ◽

F. Nex ◽

M. Gerke ◽

G. Vosselman

Keyword(s):

Urban Areas ◽

Urban Environments ◽

Global Navigation Satellite Systems ◽

Aerial Images ◽

Mobile Mapping ◽

High Resolution Data ◽

Satellite Systems ◽

Correct Orientation ◽

Fully Automatic ◽

Global Navigation Satellite

Mobile Mapping (MM) has gained significant importance in the realm of high-resolution data acquisition techniques. MM is able to record georeferenced street-level data in a continuous (laser scanners) and/or discrete (cameras) fashion. MM’s georeferencing relies on a conjunction of Global Navigation Satellite Systems (GNSS), Inertial Measurement Units (IMU) and optionally on odometry sensors. While this technique does not pose a problem for absolute positioning in open areas, its reliability and accuracy may be diminished in urban areas where high-rise buildings and other tall objects can obstruct the direct line-of-sight between the satellite and the receiver unit. Consequently, multipath measurements or complete signal outages impede the MM platform’s localisation and may affect the accurate georeferencing of collected data. This paper presents a technique to recover correct orientation parameters for MM imaging platforms by utilising aerial images as an external georeferencing source. This is achieved by a fully automatic registration strategy which takes into account the overall differences between aerial and MM data, such as scale, illumination, perspective and content. Based on these correspondences, MM data can be verified and/or corrected by using an adjustment solution. The registration strategy is discussed and results in a success rate of about 95&thinsp;%.

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Intelligent Urban Positioning: Integration of Shadow Matching with 3D-Mapping-Aided GNSS Ranging

Journal of Navigation ◽

10.1017/s0373463317000509 ◽

2017 ◽

Vol 71 (1) ◽

pp. 1-20 ◽

Cited By ~ 18

Author(s):

Mounir Adjrad ◽

Paul D. Groves

Keyword(s):

Urban Areas ◽

Three Dimensional ◽

Covariance Matrices ◽

Global Navigation Satellite Systems ◽

3D Mapping ◽

Deterministic Approach ◽

Satellite Systems ◽

Gnss Positioning ◽

Error Covariance ◽

Global Navigation Satellite

In dense urban areas, conventional Global Navigation Satellite Systems (GNSS) positioning can exhibit errors of tens of metres due to the obstruction and reflection of the signals by the surrounding buildings. By using Three-Dimensional (3D) mapping of the buildings, the accuracy can be significantly improved. This paper demonstrates the first integration of GNSS shadow matching with 3D-mapping-aided GNSS ranging. The integration is performed in the position domain, whereby separate ranging and shadow matching position solutions are computed, then combined using direction-dependent weighting. Two weighting strategies are compared, one based on the computation of ranging-based and shadow matching position error covariance matrices, and a deterministic approach based on the street azimuth. Using experimental data collected from a u-blox GNSS receiver, it is shown that both integrated position solutions are significantly more accurate than either shadow matching or 3D-mapping-aided ranging on their own. The overall Root Mean Square (RMS) horizontal accuracy obtained using covariance-based weighting was 6·1 m, a factor of four improvement on the 25·9 m obtained using conventional GNSS positioning. Results are also presented using smartphone data, where shadow matching is integrated with conventional GNSS positioning.

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Integrity Analysis for GPS-Based Navigation of UAVs in Urban Environment

Robotics ◽

10.3390/robotics9030066 ◽

2020 ◽

Vol 9 (3) ◽

pp. 66

Author(s):

Oguz Kagan Isik ◽

Juhyeon Hong ◽

Ivan Petrunin ◽

Antonios Tsourdos

Keyword(s):

Urban Environment ◽

Urban Areas ◽

Environmental Parameters ◽

Urban Environments ◽

Rate Of Change ◽

Global Navigation Satellite Systems ◽

Integrity Monitoring ◽

Satellite Systems ◽

Joint Consideration ◽

Global Navigation Satellite

The increasing use of Unmanned Aerial Vehicles (UAVs) in safety-critical missions in both civilian and military areas demands accurate and reliable navigation, where one of the key sources of navigation information is presented by Global Navigation Satellite Systems (GNSS). In challenging conditions, for example, in urban areas, the accuracy of GNSS-based navigation may degrade significantly due to user-satellite geometry and obscuration issues without being noticed by the user. Therefore, considering the essentially dynamic rate of change in this type of environment, integrity monitoring is of critical importance for understanding the level of trust we have in positioning and timing data. In this paper, the dilution of precision (DOP) coefficients under nominal and challenging conditions were investigated for the purpose of integrity monitoring in urban environments. By analyzing positioning information in a simulated urban environment using a software-based GNSS receiver, the integrity monitoring approach based on joint consideration of GNSS observables and environmental parameters has been proposed. It was shown that DOP coefficients, when considered together with a number of visible satellites and cut-off elevations specific to the urban environment carry valuable integrity information that is difficult to get using existing integrity monitoring approaches. This has allowed generating indirect integrity measures based on cut-off elevation and satellite visibility that can be used for UAV path planning and guidance in urban environments.

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Statistical models to provide meaningful information to GNSS users in the presence of ionospheric scintillation

GPS Solutions ◽

10.1007/s10291-020-01083-x ◽

2021 ◽

Vol 25 (2) ◽

Author(s):

S. Vadakke Veettil ◽

M. Aquino

Keyword(s):

Statistical Models ◽

Mean Squared Error ◽

Global Navigation Satellite Systems ◽

Ionospheric Scintillation ◽

Phase Measurements ◽

Satellite Systems ◽

Positioning Errors ◽

3D Positioning ◽

Geometric Dilution Of Precision ◽

Global Navigation Satellite

AbstractIonospheric scintillation is one of the most challenging problems in Global Navigation Satellite Systems (GNSS) positioning and navigation. Scintillation occurrence can not only lead to an increase in the probability of losing the GNSS signal lock but also reduce the precision of the pseudorange and carrier phase measurements, thus leading to positioning accuracy degradation. Statistical models developed to estimate the probability of loss of lock and Geometric Dilution of Precision normalized 3D positioning errors as a function of scintillation levels are presented. The models were developed following the statistical approach of nonlinear regression on data recorded by Ionospheric Scintillation Monitoring Receivers operational at high and low latitudes. The validation of the probability of loss of lock models indicated average correlation coefficient values above 0.7 and average Root Mean Squared Error (RMSE) values below 0.35. The validation of the positioning error models indicated average RMSE values below 10 cm. The good performance of the developed models indicates that these can provide GNSS users with information on the satellite loss of lock probability and the error in the 3D position under scintillation.

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First Evidence of Mesoscale Ocean Eddies Signature in GNSS Reflectometry Measurements

Remote Sensing ◽

10.3390/rs12030542 ◽

2020 ◽

Vol 12 (3) ◽

pp. 542 ◽

Cited By ~ 1

Author(s):

Mostafa Hoseini ◽

Milad Asgarimehr ◽

Valery Zavorotny ◽

Hossein Nahavandchi ◽

Chris Ruf ◽

...

Keyword(s):

Sensible Heat Flux ◽

Global Navigation Satellite Systems ◽

Contributing Factors ◽

Ancillary Data ◽

Satellite Systems ◽

Weather Forecasts ◽

Ocean Eddies ◽

Mesoscale Ocean Eddies ◽

Gnss Reflectometry ◽

Global Navigation Satellite

Feasibility of sensing mesoscale ocean eddies using spaceborne Global Navigation Satellite Systems-Reflectometry (GNSS-R) measurements is demonstrated for the first time. Measurements of Cyclone GNSS (CYGNSS) satellite missions over the eddies, documented in the Aviso eddy trajectory atlas, are studied. The investigation reports on the evidence of normalized bistatic radar cross section ( σ 0 ) responses over the center or the edges of the eddies. A statistical analysis using profiles over eddies in 2017 is carried out. The potential contributing factors leaving the signature in the measurements are discussed. The analysis of GNSS-R observations collocated with ancillary data from the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis-5 (ERA-5) shows strong inverse correlations of σ 0 with the sensible heat flux and surface stress in certain conditions.

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Shadow Matching: A New GNSS Positioning Technique for Urban Canyons

Journal of Navigation ◽

10.1017/s0373463311000087 ◽

2011 ◽

Vol 64 (3) ◽

pp. 417-430 ◽

Cited By ~ 134

Author(s):

Paul D. Groves

Keyword(s):

Urban Areas ◽

Tall Buildings ◽

Global Navigation Satellite Systems ◽

Positioning System ◽

Positioning Accuracy ◽

Satellite Systems ◽

Building Models ◽

Satellite Visibility ◽

Global Navigation Satellite ◽

Cross Track

The Global Positioning System (GPS) is unreliable in dense urban areas, known as urban canyons, which have tall buildings or narrow streets. This is because the buildings block the signals from many of the satellites. Combining GPS with other Global Navigation Satellite Systems (GNSS) significantly increases the availability of direct line-of-sight signals. Modelling is used to demonstrate that, although this will enable accurate positioning along the direction of the street, the positioning accuracy in the cross-street direction will be poor because the unobstructed satellite signals travel along the street, rather than across it. A novel solution to this problem is to use 3D building models to improve cross-track positioning accuracy in urban canyons by predicting which satellites are visible from different locations and comparing this with the measured satellite visibility to determine position. Modelling is used to show that this shadow matching technique has the potential to achieve metre-order cross-street positioning in urban canyons. The issues to be addressed in developing a robust and practical shadow matching positioning system are then discussed and solutions proposed.

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