GNSS Localization Propagation Error Estimation Considering Environmental Conditions

Tang, Dezhang; Lu, Debiao; Cai, Bin; Wang, Jian

doi:10.1109/itst.2018.8566771

Cited by 6 publications

(5 citation statements)

References 3 publications

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“…All the error sources mentioned above are almost constant and can be equalized by available mitigation models. However, k e is the error factor associated with k th satellite due to working environment that is random and unpredictable in nature and has the most severe effects on GNSS position estimation [38]. The error due to environment alone can be can be modelled as…”

Section: Performance Evaluation Methodologymentioning

confidence: 99%

Adaptive GNSS Receiver Design for Highly Dynamic Multipath Environments

et al. 2020

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Ubiquitous navigation requires timely, uninterrupted and accurate estimate of receiver's position at all times, in all environments and for all modes of transportation and it is highly dependent on satellite availability, geometry and accurate positioning estimation. However, the availability, continuity and accuracy of a GNSS can be severely affected in a highly dynamic environment due to blockage, fading and multipath. This results in positioning information inaccurate, unreliable and sometimes unavailable. This paper presents a study on the potential vulnerabilities that can affect a multi-constellation multi-frequency GNSS receiver in low to highly dynamic multipath environments such as clear line-of-sight, partially and highly obstructed environments to characterize the distortions/anomalies which could significantly affect the satellite signals and their impact on positioning and navigation. The multi-constellation multi-frequency GNSS receiver configuration in this paper is set to GPS, GLONASS, Galileo and BeiDou for the first time at full capacity and performance comparison is made with the GPS based on satellite availability, blockage, continuity, precision and accuracy parameters. These parameters are then used in this paper to detect and characterize the type of environment for the multi-constellation GNSS receiver without using any external aiding devices or sensors. Based on environment detection and characterization, a new Adaptive Environment Navigation (AEN) based GNSS receiver design is proposed which can work in real time and has achieved an overall availability and accuracy factor of 94% in highly dynamic multipath/NLOS environment along with a reduction in the blockage coefficient, β, by almost 11% resulting in more accuracy and precision than the standard multi-constellation GNSS receiver where the availability factor was found to be 57% only.

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Section: Performance Evaluation Methodologymentioning

confidence: 99%

Adaptive GNSS Receiver Design for Highly Dynamic Multipath Environments

et al. 2020

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“…In literature, there are studies proposing to use of different additional sensors to detect the environmental obstacle and obtain which satellite signals should be blocked from these obstacles and which signals should reach the receiver directly. Tang [11] used a fisheye camera and projection of the satellite position to obtain visibility of the satellite. Using the information on which satellite is blocked could be determined, and a User Equivalent Range Error (UERE) calculation was proposed to predict the positioning error with the Horizontal Dilution of Precision (HDOP) estimation approach.…”

Section: B Additional Sensor-based Techniquesmentioning

confidence: 99%

Performance Enhancement of Low-Cost INS/GNSS Navigation System Operating in Urban Environments

Ozdemir

Işık

Geragersian

et al. 2023

AIAA SCITECH 2023 Forum

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As a result of the increasing usage of UAVs (Unmanned Air Vehicles) in urban environments for UAM (Urban Air Mobility) applications, the preciseness and reliability of PNT (Positioning, Navigation and Timing) systems have critical importance for mission safety and success. With its high accuracy and global coverage, GNSS (Global Navigation Satellite System) is the primary PNT source for UAM applications. However, GNSS is highly vulnerable to Non-Line-of-Sight (NLoS) blockages and multipath (MP) reflections, which are quite common, especially in urban areas. This study proposes a machine learning-based NLoS/MP detection and exclusion algorithm using GNSS observables to enhance position estimations at the receiver level. By using the ensemble machine learning algorithm with the proposed method, overall 93.2% NLoS/MP detection accuracy was obtained, and 29.8% accuracy enhancement was achieved by excluding these detected signals.1 Graduate student, School of Aerospace, Transport and Manufacturing (SATM) 2 PhD candidate, School of Aerospace, Transport and Manufacturing (SATM) 3 Senior Lecturer, Centre for Autonomous and Cyberphysical Systems

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“…The density of the water vapor changes in accordance with the weather condition, in such conditions the modeling becomes difficult. Also, a dry atmosphere causes a delay [30]- [32].…”

Section: Signal Propagation Modeling Errormentioning

confidence: 99%

“…When the satellite gets to the lower levels of the atmosphere, the power signal reduces, and hence the multipath error tends to increase, which can be controlled with high-end receivers. User range error is the square root of the sum of squares of the range error of control segment, atmospheric propagation, and receiver noise and multipath [30], [36].…”

Section: Measurement Errormentioning

confidence: 99%

Study of positioning estimation with user position affected by outlier: a case study of moving-horizon estimation filter

et al. 2022

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Many applications which require accurate location point positioning systems utilize global position system for pseudosciences. One of the main challenges faced by the system occurs due to the inherent errors that are a resultant of outliers. This considerably reduces the accuracy of the observations of global position system device. In this paper, we briefly introduce the problem of position estimation when the pseudo range measurements have an outlier. Moving horizon estimation algorithm has been adapted for the simulation result compared with the extended Kalman filter model, which is still imperfect for the case of outlier. The point at which a pseudo range becomes an outlier is considered at a fixed time instance. A simulation example is presented using an existing model with a moving horizon estimator and an extended Kalman filter. The moving horizon filter turns to be more robust than Kalman filtering with presence of outlier under certain choice of tunning parameter.

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GNSS Localization Propagation Error Estimation Considering Environmental Conditions

Cited by 6 publications

References 3 publications

Adaptive GNSS Receiver Design for Highly Dynamic Multipath Environments

Adaptive GNSS Receiver Design for Highly Dynamic Multipath Environments

Performance Enhancement of Low-Cost INS/GNSS Navigation System Operating in Urban Environments

Study of positioning estimation with user position affected by outlier: a case study of moving-horizon estimation filter

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