Robust Gaussian Process Regression for Real-Time High Precision GPS Signal Enhancement

Lin, Minren; Song, Xiaomin; Qian, Quan; Li, Hao; Sun, Liang; Zhu, Shenghuo; Jin, Rong

doi:10.1145/3292500.3330695

Cited by 10 publications

(11 citation statements)

References 21 publications

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“…Another attempt using a data-mining approach to enhance the GPS signal in order to overcome the degradation of the positioning accuracy due to noise of satellite-based positioning system is made in [22]. Authors in [22] build a large-scale precision GPS receiver grid system to collect real-time GPS signals for training. Gaussian process regression is chosen to model the vertical total electron content distribution of the ionosphere of the Earth.…”

Section: ) Positioning Uncertainty Onlymentioning

confidence: 99%

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Enhanced MDT-Based Performance Estimation for AI Driven Optimization in Future Cellular Networks

2020

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Minimization of drive test (MDT) allows coverage estimation at a base station by leveraging measurement reports gathered at the user equipment (UE) without the need for drive tests. Therefore, MDT is a key enabling feature for data and artificial intelligence driven autonomous operation and optimization in future cellular networks. However, to date, the utility of MDT feature remains thwarted by issues such as sparsity of user reports and user positioning inaccuracy. We characterize three key types of errors in MDT-based coverage estimation that stem from inaccurate user positioning, scarcity of user reports and quantization. For the first time, the presented analysis shows existence of joint interplay between these errors on coverage estimation that result from inter-dependency between positioning error and bin width. The analysis also shows that there exists an optimal bin width for given user positioning inaccuracy and user density that minimizes the overall error in MDT-based estimated coverage. Utility of our framework is presented by addressing two applications from network optimization perspective: determining optimal bin width to maximize accuracy of MDT-based coverage estimation and its calibration to further improve its accuracy. INDEX TERMS Minimization of drive test, autonomous coverage estimation, optimal bin width, sparse data, coverage calibration, data driven optimization

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Section: ) Positioning Uncertainty Onlymentioning

confidence: 99%

“…where { i }, i 1 is a sequence of scalar step sizes, until a stopping criteria is reached. The shrink function in (22) applies a soft-thresholding rule at level ⌘ to the singular values of the input matrix. It is defined as…”

Section: ) Rank Minimization Based Matrix Completionmentioning

confidence: 99%

Enhanced MDT-Based Performance Estimation for AI Driven Optimization in Future Cellular Networks

2020

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“…Gaussian process regression is a nonparametric, probabilistic, Bayesian approach based on kernels. This technique was used for classification [37] and regression [38] in different domains. In our regression analysis, GPR was used in both N and water experiments, for modeling the spectral data.…”

Section: Data Preprocessing and Modelingmentioning

confidence: 99%

A Cost-Effective and Portable Optical Sensor System to Estimate Leaf Nitrogen and Water Contents in Crops

Habibullah

Mohebian

Soolanayakanahally

et al. 2020

Sensors

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Non-invasive determination of leaf nitrogen (N) and water contents is essential for ensuring the healthy growth of the plants. However, most of the existing methods to measure them are expensive. In this paper, a low-cost, portable multispectral sensor system is proposed to determine N and water contents in the leaves, non-invasively. Four different species of plants—canola, corn, soybean, and wheat—are used as test plants to investigate the utility of the proposed device. The sensor system comprises two multispectral sensors, visible (VIS) and near-infrared (NIR), detecting reflectance at 12 wavelengths (six from each sensor). Two separate experiments were performed in a controlled greenhouse environment, including N and water experiments. Spectral data were collected from 307 leaves (121 for N and 186 for water experiment), and the rational quadratic Gaussian process regression (GPR) algorithm was applied to correlate the reflectance data with actual N and water content. By performing five-fold cross-validation, the N estimation showed a coefficient of determination ( R 2 ) of 63.91% for canola, 80.05% for corn, 82.29% for soybean, and 63.21% for wheat. For water content estimation, canola showed an R 2 of 18.02%, corn showed an R 2 of 68.41%, soybean showed an R 2 of 46.38%, and wheat showed an R 2 of 64.58%. The result reveals that the proposed low-cost sensor with an appropriate regression model can be used to determine N content. However, further investigation is needed to improve the water estimation results using the proposed device.

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“…The GP framework presented many advantages over competing modeling strategies, such as providing powerful and convenient ways of incorporating prior knowledge and requiring less training data than neural networks. Another recent study shows the GP ability to enhance the positioning performance by improved TEC estimation in real-time Lin et al (2019).…”

Section: Introductionmentioning

confidence: 99%

Gauss process regression for real-time ionospheric delay estimation from GNSS observations

Lupsic

Takács

2022

Acta Geod Geophys

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The number of devices equipped with global satellite positioning has exceeded seven billion recently. There are a wide variety of receivers regarding their accuracy and reliability. Low cost, multi-frequency units have been released on the market latterly; however, the number of single-frequency receivers is still significant. Since their measurements are influenced by ionospheric delay, accurate ionosphere models are of utmost importance to reduce the effect. This paper summarizes how Gauss process regression (GPR) can be applied to derive near real-time regional ionosphere models using raw Global Navigation Satellite System (GNSS) observations of permanent stations. While Gauss process is widely used in machine learning, GPR is a nonparametric, Bayesian approach to regression. GPR has several benefits for ionosphere monitoring since it is quite robust and efficient to derive a grid model from data available in irregular set of ionospheric pierce points. The corresponding instrumental delays are estimated by a parallel Kalman filter. The presented algorithm can be applied near real-time, however the results are offline calculated and are compared to two high quality TEC map products. Based on the analysis, the accuracy of the GPR modell is in 2 TECu range. The developed methods could be efficiently applied in the field of autonomous vehicle navigation with meeting both accuracy and integrity requirements.

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Robust Gaussian Process Regression for Real-Time High Precision GPS Signal Enhancement

Cited by 10 publications

References 21 publications

Enhanced MDT-Based Performance Estimation for AI Driven Optimization in Future Cellular Networks

Enhanced MDT-Based Performance Estimation for AI Driven Optimization in Future Cellular Networks

A Cost-Effective and Portable Optical Sensor System to Estimate Leaf Nitrogen and Water Contents in Crops

Gauss process regression for real-time ionospheric delay estimation from GNSS observations

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