GNSS positioning in non-line-of-sight context—A survey

Breßler, Julia; Reisdorf, Pierre; Obst, Marcus; Wanielik, Gerd

doi:10.1109/itsc.2016.7795701

Cited by 41 publications

(23 citation statements)

References 47 publications

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“…If the direct light‐of‐sight (LOS) is blocked and reflected signals from the same satellite are received, the notorious non‐light‐of‐sight (NLOS) receptions occur. NLOS is the dominant GNSS positioning error in the cities mentioned above . As a result, the positioning error can go up to even 100 m …”

Section: Introductionmentioning

confidence: 99%

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Correcting NLOS by 3D LiDAR and building height to improve GNSS single point positioning

2019

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We present a novel method to detect the GNSS NLOS and correct the NLOS pseudorange measurements based on on-board sensing. This paper demonstrates the use of LiDAR scanner and a list of building heights to describe the perceived environment. To estimate the geometry and pose of the top edges of buildings (TEBs) relative to the GNSS receiver, a surface segmentation method is employed to detect the TEBs of surrounding buildings using 3D LiDAR point clouds. The top edges of the building are extracted and extended using the building height list in Skyplot to identify the NLOSaffected ones. Innovatively, the NLOS delay in pseudorange is corrected based on the detected TEBs. Weighted least squares (WLS) is used to cooperate the corrected NLOS and other pseudorange measurements. Vehicle experiments are conducted in two different urban canyons to verify the effectiveness of the proposed method in improving GNSS single point positioning (SPP) accuracy.

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Section: Introductionmentioning

confidence: 99%

“…According to a recent review paper, NLOS is currently the major difficulty in using GNSS for intelligent transportation. Due to NLOS, the performance of GNSS positioning becomes highly related to environmental features, such as buildings.…”

Section: Introductionmentioning

confidence: 99%

Correcting NLOS by 3D LiDAR and building height to improve GNSS single point positioning

2019

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“…Recent research dealing with multipath and NLOS utilizes 3D mapping and is called as 3D mapping aided (3DMA) positioning methods. Comprehensive related work on 3DMA positioning method can be found at Groves et al (2015) and Breßler et al (2016). One of the most well-known 3DMA methods is shadow matching proposed by university college London (UCL).…”

Section: Figmentioning

confidence: 99%

Analysis and modeling GPS NLOS effect in highly urbanized area

Hsu

2017

GPS Solut

150

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Current GPS positioning accuracy in urban areas is still unsatisfactory for various applications, 7 including pedestrian navigation and autonomous driving. Due to the ineffectiveness of special 8 corrector designs against non-line-of-sight (NLOS) reception, the research regarding NLOS signals has been increasing in the recent years. This study first develops an algorithm to detect NLOS signals from the pseudorange measurements by using a 3D building model, ray-tracing simulation, and known receiver position. According to the analysis of 24 hours of collected NLOS data, a new finding is that NLOS pseudorange delay is highly correlated with the elevation angle of satellite instead of the received signal strength. Thus, we further propose an innovative NLOS model using two variables, the elevation angle and the distance between the receiver and building that reflect the NLOS. The proposed model is evaluated in both pseudorange and position domains. Based on the experiment results regarding pseudorange error, the difference between the proposed model and the collected NLOS measurement is very small. Finally, the proposed model is applied to a hypothesis based positioning method and achieves about 6.3 meters in terms of horizontal positioning accuracy, which is only slightly worse than the method applied with ray-tracing simulation.

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“…The vertical position of the vehicle is usually assumed as the summation of the elevation and vehicle height, as the 3DMA GNSS is usually implemented by hypothesis-based estimation methods such as particle filter. Thus, the computation load of 3-dimensional particles is excessive [33]. In addition, 3D mapping of urban viaducts is too complex for current 3DMA methods.…”

Section: Viaduct Heightmentioning

confidence: 99%

Intelligent Viaduct Recognition and Driving Altitude Determination Using GPS Data

Hsu

Kamijo

2017

IEEE Trans. Intell. Veh.

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Abstract-The rapid development of road networks in highly urbanized cities requires a substantial number of viaducts to reduce the increasing traffic burden on urban highways. Unfortunately, this road design deteriorates the performance of global positioning system (GPS) navigators due to the signal blockage between satellite and receiver. As a result, it is difficult for GPS navigators to determine whether the vehicle is driving on the street or viaduct. Misleading information could confuse drivers and lead them to drive irregularly, which is dangerous in heavy traffic. This paper proposes a novel classification algorithm based on the fact that different satellite signals and conditions can be observed in the on-street and on-viaduct cases by the implementation of dynamic Bayesian network (DBN) to distinguish the driving area of a vehicle. In addition, the proposed DBN can also accurately estimate the driving altitude of the vehicle according to the experiment results.

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GNSS positioning in non-line-of-sight context—A survey

Cited by 41 publications

References 47 publications

Correcting NLOS by 3D LiDAR and building height to improve GNSS single point positioning

Correcting NLOS by 3D LiDAR and building height to improve GNSS single point positioning

Analysis and modeling GPS NLOS effect in highly urbanized area

Intelligent Viaduct Recognition and Driving Altitude Determination Using GPS Data

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