SBAS-Aided GPS Positioning with an Extended Ionosphere Map at the Boundaries of WAAS Service Area

Kim, Mingyu; Kim, Jeongrae

doi:10.3390/rs13010151

Cited by 9 publications

(4 citation statements)

References 25 publications

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“…In order to enhance and reduce the parameter scores, the REDM parameter calculated through the EM algorithm is overbounding. The 𝜎 1 is greater than 𝜎 2 , then the overbounding model structure is shown in (5)…”

Section: Single-frequency Reom Formationmentioning

confidence: 99%

“…The GBAS verifies the integrity in three ways: delivering disparity correlations of local-area and elimination of generalmode errors; delivering the authorized user through a personalized monitor for system faults; and enabling the user to create a residual error bound to make navigation actions [4]. Due to free electrons, the ionospheric delay along the path of the Global Navigation Satellite System (GNSS) signal is circulated consistently under normal conditions [5]. Whenever GNSS signals travel with the circulated ionosphere, a severe error that potentially compromises the truthfulness of GBAS is observed [6].…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Single-frequency Reom Formationmentioning

confidence: 99%

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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“…The Relative amount of chlorophyll is one of the parameters that can describe plant health conditions. Kim research [9] shows that the NDVI value of the multispectral sensor has a high correlation with leaf chlorophyll status indicated by the r-square value 0.941 at the beginning and end of the growth phase. The multispectral UAV model effectively represents the calculation of the damaged area on agricultural land.…”

Section: Introductionmentioning

confidence: 95%

Assessing Paddy Field Health Using High-Resolution Multi-Spectral Camera in Subang Indonesia to Implement Precision Agriculture 4.0

The,

Nurmansyah,

Sartiami

et al. 2023

IOP Conf. Ser.: Earth Environ. Sci.

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Increasing yield and improving the sustainability of agriculture are needed through data-driven precision agriculture technology. Multispectral sensor measures the light reflected from the plant canopy and generates a reflectance value that indicates the difference between healthy and stressed plants. We use precision agriculture to assess paddy field health in Subang Regency, West Java, Indonesia, especially early detection of bacterial leaf blight and stem borer disease. The condition of the study area has a relatively flat topography, with the Grendel glutinous paddy variety, and has a late vegetative to early generative phase. The data was gathered using an unmanned aerial vehicle (UAV) fitted with a MicaSense Red Edge-P sensor to obtain multispectral images. We employed Normalized Difference Red Edge Index (NDRE) to create a health model and validated using 70 field sampling locations. As a result, accuracy detection was 90% hence the use of UAV technology can be used for early disease detection. We classified the paddy field as healthy and unhealthy in percentage through spectral analysis and data from validation. This research has proven that we could detect disease in paddy fields faster and more efficiently. Utilizing UAVs for precision agriculture will prevent crop failure and increase food security in Subang and Indonesia. However, the limitation of this study is weather conditions and should consider plant varieties.

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“…Since ionospheric delay is a critical error source in GNSSs, it is also a principal aspect of SBAS corrections. Unlike orbit and clock corrections provided for each satellite, ionospheric corrections are generated for each pre-defined geographical location with a resolution of 5°× 5°, known as the Ionosphere Grid Points (IGPs) [3,7,9]. IGPs located at an altitude of 350km in a thin shell over WGS 84 ellipsoid are widely adopted by SBASs [7,10,11].…”

Section: Introductionmentioning

confidence: 99%

Artificial Neural Network-Based Ionospheric Delay Correction Method for Satellite-Based Augmentation Systems

Wang

Sun

2022

Remote Sensing

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Ionospheric delay is a critical error source in Global Navigation Satellite Systems (GNSSs) and a principal aspect of Satellite Based Augmentation System (SBAS) corrections. Grid Ionospheric Vertical Delays (GIVDs) are derived from the delays on Ionosphere Pierce Points (IPPs), which are observed by SBAS reference stations. SBAS master stations calculate ionospheric delay corrections by several methods, such as planar fit or Kriging. However, when there are not enough IPPs around an Ionosphere Grid Point (IGP) or the IPPs are unevenly distributed, the fitting accuracy of planar fit or Kriging is unsatisfactory. Moreover, the integrity bounds of Grid Ionospheric Vertical Errors (GIVEs) are overly conservative. Since Artificial Neural Networks (ANNs) are widely used in ionospheric research due to their self-adaptation, parallelism, non-linearity, robustness, and learnability, the ANN method for GIVD and GIVE derivation is proposed in this article. Networks are separately trained for IGPs, and five years of historical data are applied on network training. Principal Component Analysis (PCA) is applied for dimensionality reduction of geomagnetic and solar indices, which is employed as a network input feature. Furthermore, the GIVE algorithm of the ANN method is derived based on the distribution of the residual random variable. Finally, experiments are conducted on 12 IGPs over the East China region. Under normal ionospheric conditions, compared with the planar fit and Kriging methods, the residual reduction of the ANN method is approximately 15%. The ANN method fits the ionospheric delay residual error better. The percentage of GIVE availability under 2.7 m increases at least 25 points in comparison to Kriging. Under disturbed conditions, due to a lack of training samples, the ANN method is incompetent compared with planar fit or Kriging.

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SBAS-Aided GPS Positioning with an Extended Ionosphere Map at the Boundaries of WAAS Service Area

Cited by 9 publications

References 25 publications

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

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

Assessing Paddy Field Health Using High-Resolution Multi-Spectral Camera in Subang Indonesia to Implement Precision Agriculture 4.0

Artificial Neural Network-Based Ionospheric Delay Correction Method for Satellite-Based Augmentation Systems

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