Analysis and modeling GPS NLOS effect in highly urbanized area

Hsu, Li‐Ta

doi:10.1007/s10291-017-0667-9

Cited by 151 publications

(106 citation statements)

References 30 publications

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“…However, this approach is more computationally intensive as the path delays cannot easily be precomputed. The processing load can be reduced by using predicted pseudoranges based on the satellite elevation angle and distance to the reflected surface . The urban trench approach presented in an existing study enables the path delays of NLOS signals to be computed very efficiently but only if the building layout is highly symmetric, so it can only be used in suitable environments.…”

Section: Review Of 3d‐mapping–aided Gnssmentioning

confidence: 99%

Performance assessment of 3D‐mapping–aided GNSS part 1: Algorithms, user equipment, and review

Groves

Adjrad

2019

NAVIGATION

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A full performance assessment of 3D‐mapping–aided (3DMA) GNSS in dense urban areas is presented. This first part of a two‐part paper focuses on the effects of algorithm design and user equipment, based on data collected in London using Leica Viva GS15 and u‐blox EVK‐M8T GNSS receivers and a Nexus 9 Android tablet. Best performance is obtained by combining shadow matching with likelihood‐based 3DMA GNSS ranging using hypothesis‐domain integration. Improved versions of the algorithms, together with a comprehensive tuning process, are described. Root mean square horizontal position errors obtained using data from Leica, u‐blox, and Nexus receivers are 3.5, 4.7, and 4.9 m, respectively, compared with 23.6, 26.4, and 31.0 m using conventional GNSS positioning, about a factor of six improvement. Optimal algorithm tuning depends on the environment and the impact of varying grid spacing of the candidate positions is assessed. Algorithms have also been shown to operate in real time on both a Raspberry Pi 3 and a Samsung Galaxy S8+ Android smartphone.

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Section: Review Of 3d‐mapping–aided Gnssmentioning

confidence: 99%

Performance assessment of 3D‐mapping–aided GNSS part 1: Algorithms, user equipment, and review

Groves

Adjrad

2019

NAVIGATION

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“…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%

“…Based on the detected TEBs, NLOS measurement can be identified. Previously, our team proposed an NLOS error model based on two parameters: the distance between the GNSS receiver and NLOS reflector and the elevation and azimuth angle of the satellite . In this paper, the distance between the GNSS receiver and reflectors can be obtained by LiDAR scanner.…”

Section: Introductionmentioning

confidence: 99%

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

2019

Self Cite

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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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“…The multipath and NLOS effects are further introducing GNSS positioning error, which can be severe in dense urban areas such as Hong Kong, Shanghai and Beijing. The NLOS error can even exceed 100 meters when operating in urban area and the differential technique can even increase the error [21]. One of the feasible solution is to identify and exclude the multipath and/or NLOS affected signals.…”

Section: Introductionmentioning

confidence: 99%

A novel GNSS based V2V cooperative localization to exclude multipath effect using consistency checks

Zhang

Wen

Hsu

2018

2018 IEEE/ION Position, Location and Navigation Symposium (PLANS)

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Global Navigation Satellite System (GNSS) is essential for autonomous driving by providing absolute positioning solutions. However, the performance of GNSS measurement is significantly degraded in urban areas, due to the severe multipath and non-line-of-sight (NLOS) effects. To ensure the operation safety of autonomous driving applications, the GNSS localization performance is required to improve. In this study, a novel vehicle-to-vehicle (V2V) based cooperative localization algorithm with double-layers of consistency check (CC) is developed. GNSS pseudorange measurements of the ego-vehicle and surrounding vehicles conduct the first-layer of CC independently to exclude the multipath-biased measurement and obtaining their absolute positions. Then, the survived GNSS measurement and the absolute positions of each vehicles are shared in between, and further applied with double difference (DD) technique to obtain an accurate relative position between the surrounding vehicles. The second-layer CC is conducted during the GNSS DD-based relative positioning, to exclude the multipath and NLOS effects more comprehensively. Finally, the absolute and relative positions of surrounding vehicles are cooperated to optimize the final position of all participating vehicles. By the both simulation and real experimental results, the proposed algorithm is able to mitigate the multipath effect and significantly improve the accuracy of GNSS localization solutions to fulfill the safety requirements for autonomous driving.

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Analysis and modeling GPS NLOS effect in highly urbanized area

Cited by 151 publications

References 30 publications

Performance assessment of 3D‐mapping–aided GNSS part 1: Algorithms, user equipment, and review

Performance assessment of 3D‐mapping–aided GNSS part 1: Algorithms, user equipment, and review

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

A novel GNSS based V2V cooperative localization to exclude multipath effect using consistency checks

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