Covariance Analysis of Real-Time Precise GPS Orbit Estimated from Double-Differenced Carrier Phase Observations

Yu, Sunkyoung; Kim, Donguk; Song, Junesol; Kee, Changdon

doi:10.3390/rs11192271

Cited by 4 publications

(1 citation statement)

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“…One of the most stringent GNSS navigation requirements, especially for safety-of-life applications, is integrity, which refers to the ability of the system to provide timely warnings to the users when the system should not be used for navigation due to fault. Receiver Autonomous Integrity Monitoring (RAIM) is a user terminal integrity monitoring system, which is always regarded as the last line of the defense for integrity [4][5][6][7]. It is embedded in most GNSS receivers that can accurately detect and identify faults from satellite observations in real time.…”

Section: Introductionmentioning

confidence: 99%

RAIM-NET: A Deep Neural Network for Receiver Autonomous Integrity Monitoring

Sun

2020

Remote Sensing

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With the continuous popularization of Global Navigation Satellite System (GNSS) in various applications, the performance requirement for integrity is also increasing, especially in the field of safety-of-life. Although the existing Receiver Autonomous Integrity Monitoring (RAIM) algorithm has been embedded in the GNSS receiver as a standard method, it might still suffer from small fault detection and delay alarm problem for time series fault models. In an effort to solve this problem, a Deep Neural Network (DNN) for RAIM, named RAIM-NET, is investigated in this paper. The main idea of RAIM-NET is to propose a combination of feature vector extraction and DNN model to improve the performance of integrity monitoring, with a problem specifically designed for loss function, obtaining the model parameters. Inspired by the powerful advantages of Recurrent Neural Network (RNN) in time series data processing, a multilayer RNN is applied to build the DNN model structure and improve the detection rate for small faults and reduce the alarm delay for the time series fault event. Finally, real GNSS data experiments are designed to verify the performance of RAIM-NET in fault detection and time delay for integrity monitoring.

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Section: Introductionmentioning

confidence: 99%

RAIM-NET: A Deep Neural Network for Receiver Autonomous Integrity Monitoring

Sun

2020

Remote Sensing

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Compact RTK for expanded area (COREA): a new method for carrier-phase-based satellite augmentation system

Kim,

Kee

2024

GPS Solut

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This study proposes a new concept of carrier-phase-based satellite augmentation system named “Compact Real-time Kinematic for Expanded Area (COREA),” which provides centimeter-level positioning services across nationwide coverage. The proposed system’s architecture is very similar to that of the satellite-based augmentation system (SBAS), a meter-level aviation safety service. While network real-time kinematic and precise-point-positioning-RTK (PPP-RTK) rely on several densely positioned reference stations, COREA provides carrier-phase-based corrections using a few reference stations with a distance of 400–1000 km. Furthermore, the COREA corrections can be transmitted by satellite signals with extremely low-speed data links of 250 bps, similar to SBAS. This study focused on the generation method for satellite code/phase clock (CPC) corrections, which is the most significant part of the system. We analyzed the user performance of the COREA system constructed in the Midwest and South of the United States with six reference stations. Consequently, satellite CPC corrections are resilient to communication failures and highly accurate in identifying user integer ambiguity. The 95% position accuracy is approximately 1.8 cm horizontally and 7.1 cm vertically, with an average convergence time of 1–3 min using only GPS triple-frequency signals. In summary, the COREA system preserves the hardware architecture of the legacy SBAS while providing centimeter-level services with fast convergence times by utilizing extremely low-speed satellite data links across the country.

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