Volcanic clouds detection applying machine learning techniques to GNSS radio occultations
Authors (first, second and last of 4)
Collection
Artificial Intelligence Applications in GNSS
With the increase of cognitive capability, modern machines can offer solutions to problems for which absolute logical certainty cannot provide an answer. Generally, Artificial Intelligence (AI) refers to the computers’ capability to imitate the learning and problem-solving attributes of human intelligence. AI techniques have become an essential part of the technology industry, helping to solve many challenging problems in computer science and software engineering research. Evolutionary algorithms, machine learning, fuzzy systems, and neural networks are important instances of AI in the engineering field. During the past few years, AI has been addressing various problems in Global Navigation Satellite Systems (GNSS), including precise positioning, navigation in complex areas, interference cancelation and mitigation. Due to the increasing rate of innovations and developments in the field of AI, we focus on new solutions for current issues of GNSS devices or enhancing the efficiency of the existing solutions. Prime interest is devoted to novel, innovative, or highly demanding uses.
Editors
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Dr. Seyed Mohammad Reza Mosavi
Mohammad-Reza Mosavi received his B.S., M.S., and Ph.D. degrees in Electronic Engineering from Iran University of Science and Technology (IUST), Tehran, Iran in 1997, 1998, and 2004, respectively. He is currently faculty member of Department of Electrical Engineering of IUST as full professor. He is the author of about 200 scientific publications on journals and international conferences. His research interests include circuits and systems design.
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Dr. Sergio Baselga
Cartographic Engineering, Geodesy and Photogrammetry Dept. Universitat Politècnica de València Camino de Vera s/n, 46022 Valencia, Spain
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Dr. Yunxiang Liu
University of Colorado Boulder, USA
Articles (43 in this collection)
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Leveraging machine learning techniques and GPS measurements for precise TEC rate predictions
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Machine learning-based detection of TEC signatures related to earthquakes and tsunamis: the 2015 Illapel case study
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Improving performances of GNSS positioning correction using multiview deep reinforcement learning with sparse representation
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Enhancing satellite clock bias prediction in BDS with LSTM-attention model
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An efficient GNSS NLOS signal identification and processing method using random forest and factor analysis with visual labels
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Machine learning approach for GNSS geodetic velocity estimation
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Global zenith wet delay modeling with surface meteorological data and machine learning
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Detection and mitigation of time synchronization attacks based on long short-term memory neural network
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Improving PPP-RTK-based vehicle navigation in urban environments via multilayer perceptron-based NLOS signal detection
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Comprehensive classification assessment of GNSS observation data quality by fusing k-means and KNN algorithms
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A new deep self-attention neural network for GNSS coordinate time series prediction
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A precipitation forecast model with a neural network and improved GPT3 model for Japan
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A refined zenith tropospheric delay model for Mainland China based on the global pressure and temperature 3 (GPT3) model and random forest
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Virtual reference station technology for voxels without signal ray in ionospheric tomography based on machine learning
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Non-holonomic constraint (NHC)-assisted GNSS/SINS positioning using a vehicle motion state classification (VMSC)-based convolution neural network
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GPS signal acquisition based on deep convolutional neural network and post-correlation methods
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Research on modeling and predicting of BDS-3 satellite clock bias using the LSTM neural network model
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A new flexible model to calibrate single-layer height for ionospheric modeling using a neural network model
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New encoder–decoder convolutional LSTM neural network architectures for next-day global ionosphere maps forecast
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GNSS Site unmodeled error prediction based on machine learning
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LSTM-based short-term ionospheric TEC forecast model and positioning accuracy analysis
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An adaptive quality monitoring method for real-time ionospheric corrections
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Spatial outlier detection on discrete GNSS velocity fields using robust Mahalanobis-distance-based unsupervised classification
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Machine learning-based snow depth retrieval using GNSS signal-to-noise ratio data
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Conventional and neural network-based water vapor density model for GNSS troposphere tomography
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A new fuzzy strong tracking cubature Kalman filter for INS/GNSS
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Clock bias prediction algorithm for navigation satellites based on a supervised learning long short-term memory neural network
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Joint time–frequency mask and convolutional neural network for real-time separation of multipath in GNSS deformation monitoring
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A new three-dimensional computerized ionospheric tomography model based on a neural network
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