Multipath Error Fusion Modeling Methods for Multi-GNSS

Zou, Xuan; Li, Zhiyuan; Wang, Yawei; Deng, Chenlong; Li, Yangyang; Tang, Wei; Fu, Ruinan; Cui, Jianhui; Liu, Jingnan

doi:10.3390/rs13152925

Cited by 12 publications

(5 citation statements)

References 24 publications

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“…Therefore, in harsh multipath environments such as navigating in a canyon deep within a city, the multipath effect-suppression algorithm must be considered. This article does not discuss the related research on multipath signal modeling or related methods, for which one can refer to [27,28].…”

Section: Gnss Receiver Measurement Modelmentioning

confidence: 99%

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GNSS-5G Hybrid Positioning Based on Joint Estimation of Multiple Signals in a Highly Dependable Spatio-Temporal Network

Liu,

Deng,

et al. 2023

Remote Sensing

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The Global Navigation Satellite System (GNSS) has been widely used in every area of our daily life to provide accurate Positioning, Navigation, and Timing (PNT) services. However, due to the multipatch effect and an obstructed view of the satellite, GNSS receivers are susceptible to large-ranging errors, which are particularly prevalent in urban areas where precise positioning is indispensable. The deployment of the high-spatial-density Fifth-Generation (5G) network makes it possible to provide a broad area with high-precision positioning service. Obviously, it promoting the deep integration of the GNSS system and the 5G mobile communication network and establishing a Highly Dependable Spatio-temporal Network (HDSN) have become an inevitable trend. The existing algorithm for the fusion of multiple signals has difficulty settling problems such as the fast fluctuation of available signal sources and the poor stability of multi-scale multi-type signal estimation in GNSS-5G hybrid networks. Here, we propose a Square Root Unscented Stable Filter (SRUSF) for GNSS and 5G joint positioning with a compact coupled filter group architecture in a highly dependable spatio-temporal network. A stabilized coefficient is added to guarantee positive covariance of the estimation error. The possibility of divergence of filtering results due to the variation in signal sources and the incomplete agreement between the system model and the actual situation are reduced. The simulation results show that the proposed SRUSF method substantially enhances the positioning accuracy and reliability compared with the other five joint estimation methods for multiple signals. This work will enable the terminal of mass users to provide timing and positioning services with unprecedented accuracy and dependability under the GNSS and 5 G-based spatio-temporal network’s architecture.

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Section: Gnss Receiver Measurement Modelmentioning

confidence: 99%

“…Incorporating across Equations ( 23) to (27) yields Equation ( 28), the fading factor introduced into the predicted state covariance should meet the requirements…”

Section: Square Root Unscented Stable Filtermentioning

confidence: 99%

GNSS-5G Hybrid Positioning Based on Joint Estimation of Multiple Signals in a Highly Dependable Spatio-Temporal Network

Liu,

Deng,

et al. 2023

Remote Sensing

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“…This truthfulness data contains covariance errors of each correlation, clock, orbit and ionosphere [10]. These covariances are utilized to define weight measurements during the position method and are employed to distinguish the error bound calculations [11]. By utilizing this error bound, the SBAS-based GNSS establishes whether or not to employ GNSS [12].…”

Section: Introductionmentioning

confidence: 99%

Overbounding Ifree errors based on bayesian Gaussian mixture model for ground-based augmentation system

Banu,

Shanavas

2024

IJECE

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A dual-frequency measurement is employed in conjunction with an innovative Ifree filtering technique for mitigating the primary sources of Ifree influence on ground-based augmentation systems (GBAS) to safeguard the reliability of GBAS. The protective level achieved through the conventional Gaussian overbounding approach that are considered as much conventional technique. This adherence to tradition results in decreased reliability and a higher likelihood of false alarms. In contrast, the utilization of the Ifree algorithm contributes to reducing errors associated with dual-frequency measurements. This paper proposes the overbounding process according to Bayesian Gaussian mixture model (GMM) for maintaining Ifree-based GBAS range error. The Bayesian GMM is utilized for single-frequency model errors to examine the ambiguity estimations. The Monte Carlo (MC) simulation is established for defining estimated GMM assurance level accuracy which is attained through the general estimation method. Then, the last Bayesian GMM which is utilized for overbounding Ifree error distribution is investigated. According to the property of convolution invariance, the vertical protection in position field is determined without presenting difficult numerical calculations.

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“…Tao et al [ 33 ] proposed a time-frequency mask and convolutional neural network to separate and mitigate the GNSS multipath error. Zou et al [ 34 ] compared the performance of the multi-point hemispherical grid model (MHGM) with SF from a multi-GNSS point of view and summarized the determination method for GNSS satellites’ orbit repeat periods. The above software methods based on the spatio-temporal repetition characteristics of the multipath effect are only used to study the GPS multipath effect.…”

Section: Introductionmentioning

confidence: 99%

BDS Dual-Frequency Carrier Phase Multipath Hemispherical Map Model and Its Application in Real-Time Deformation Monitoring

Sun

Zhang

Gào

et al. 2023

Sensors

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The BDS multipath delay error is highly related to the surrounding monitoring environment, which cannot be eliminated or mitigated by applying the double difference observation model. In the actual monitoring environment, due to the complexity of the BDS constellation, it is difficult for existing algorithms to consider GEO, IGSO, MEO and other different orbital types of satellites for real-time and efficient multipath error reduction. Therefore, we propose a novel BDS dual-frequency multipath error reduction method for real deformation monitoring for BDS considering various satellite orbit types. This method extracts the single error residual of each satellite based on the assumption of “zero mean” and divides the appropriate grid density of GEO and IGSO/MEO, respectively, to construct a dual-frequency multipath hemispherical map model suitable for BDS satellites with different orbital types. This method can realize the multipath error elimination of the observed values of different orbits and different frequencies. The results of simulation experiments and real deformation monitoring data demonstrate that this method can effectively eliminate low-frequency multipath delay errors in the observation domain and coordinate domain. After multipath correction, the precision of the horizontal coordinates and height coordinates are 1.7 mm and 4.6 mm. The precision of the horizontal coordinate and height coordinate is increased by 50% and 60%, respectively. The fixed rate of ambiguity increased by 5–7%.

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Multipath Error Fusion Modeling Methods for Multi-GNSS

Cited by 12 publications

References 24 publications

GNSS-5G Hybrid Positioning Based on Joint Estimation of Multiple Signals in a Highly Dependable Spatio-Temporal Network

GNSS-5G Hybrid Positioning Based on Joint Estimation of Multiple Signals in a Highly Dependable Spatio-Temporal Network

Overbounding Ifree errors based on bayesian Gaussian mixture model for ground-based augmentation system

BDS Dual-Frequency Carrier Phase Multipath Hemispherical Map Model and Its Application in Real-Time Deformation Monitoring

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