scholarly journals A SEQUENCE-TO-SEQUENCE TEMPORAL CONVOLUTIONAL NEURAL NETWORK FOR IONOSPHERE PREDICTION USING GNSS OBSERVATIONS

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 813-820
Author(s):  
M. Kaselimi ◽  
N. Doulamis ◽  
A. Doulamis ◽  
D. Delikaraoglou
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Electron Content ◽  
Complex Data ◽  
Navigation Satellite ◽  
Levels Of Abstraction ◽  
Total Electron ◽  
Total Electron Content Data ◽  
Model Complex ◽  
Gnss Observations

Abstract. This paper proposes a model suitable for predicting the ionosphere delay at different locations of receiver stations using a temporal 1D convolutional neural network (CNN) model. CNN model can optimally addresses non-linearity and model complex data through the creation of powerful representations at hierarchical levels of abstraction. To be able to predict ionosphere values for each visible satellite at a given station, sequence-to-sequence (seq2seq) models are introduced. These models are commonly used for solving sequential problems. In seq2seq models, a sequential input is entered to the model and the output has also a sequential form. Adopting this structure help us to predict ionosphere values for all satellites in view at every epoch. As experimental data, we used global navigation satellite system (GNSS) observations from selected sites in central Europe, of the global international GNSS network (IGS). The data used are part of the multi GNSS experiment (MGEX) project, that provides observations from multiple navigation satellite systems. After processing with precise point positioning (PPP) technique as implemented with GAMP software, the slant total electron content data (STEC) were obtained. The proposed CNN uses as input the ionosphere pierce points (IPP) points coordinates per visible satellite. Then, based on outcomes of the ionosphere parameters, the temporal CNN is deployed to predict future TEC variations.

An adaptive convolutional neural network model for ionosphere prediction

2020 ◽  
Author(s):  
Maria Kaselimi ◽  
Nikolaos Doulamis ◽  
Demitris Delikaraoglou
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Electron Density ◽  
Electron Content ◽  
Complex Data ◽  
Navigation Satellite ◽  
Ionospheric Electron Density ◽  
Satellite Systems ◽  
Wide Range ◽  
Adaptive Convolutional Neural Network

<p>Knowledge of the ionospheric electron density is essential for a wide range of applications, e.g., telecommunications, satellite positioning and navigation, and Earth observation from space. Therefore, considerable efforts have been concentrated on modeling this ionospheric parameter of interest. Ionospheric electron density is characterized by high complexity and is space−and time−varying, as it is highly dependent on local time, latitude, longitude, season, solar cycle and activity, and geomagnetic conditions. Daytime disturbances cause periodic changes in total electron content (diurnal variation) and additionally, there are multi-day periodicities, seasonal variations, latitudinal variations, or even ionospheric perturbations that cause fluctuations in signal transmission.</p><p>Because of its multiple band frequencies, the current Global Navigation Satellite Systems (GNSS) offer an excellent example of how we can infer ionosphere conditions from its effect on the radiosignals from different GNSS band frequencies. Thus, GNSS techniques provide a way of directly measuring the electron density in the ionosphere. The main advantage of such techniques is the provision of the integrated electron content measurements along the satellite-to-receiver line-of-sight at a large number of sites over a large geographic area.</p><p>Deep learning techniques are essential to reveal accurate ionospheric conditions and create representations at high levels of abstraction. These methods can successfully deal with non-linearity and complexity and are capable of identifying complex data patterns, achieving accurate ionosphere modeling. One application that has recently attracted considerable attention within the geodetic community is the possibility of applying these techniques in order to model the ionosphere delays based on GNSS satellite signals.</p><p>This paper deals with a modeling approach suitable for predicting the ionosphere delay at different locations of the IGS network stations using an adaptive Convolutional Neural Network (CNN). As experimental data we used actual GNSS observations from selected stations of the global IGS network which were participating in the still-ongoing MGEX project that provides various satellite signals from the currently available multiple navigation satellite systems. Slant TEC data (STEC) were obtained using the undifferenced and unconstrained PPP technique. The STEC data were provided by GAMP software and converted to VTEC data values. The proposed CNN uses the following basic information: GNSS signal azimuth and elevation angle, GNSS satellite position (x and y). Then, the adaptive CNN utilizes these data inputs along with the predicted VTEC values of the first CNN for the previous observation epochs. Topics to be discussed in the paper include the design of the CNN network structure, training strategy, data analysis, as well as preliminary testing results of the ionospheric delays predictions as compared with the IGS ionosphere products.   </p>

Recurrent Neural Networks for Ionospheric Time Delays Prediction Using Global Navigation Satellite System Observables

2021 ◽  
Author(s):  
Maria Kaselimi ◽  
Nikolaos Doulamis ◽  
Demitris Delikaraoglou
Keyword(s):  
Global Navigation Satellite System ◽  
Satellite System ◽  
Electron Content ◽  
Navigation Satellite ◽  
Total Electron ◽  
Proposed Model ◽  
Model Complex ◽  
Global Navigation ◽  
Global Navigation Satellite

<p>Total Electron Content (TEC) is the integral of the location-dependent electron density along the signal path and is a crucial parameter that is often used to describe ionospheric variability, as it is strongly affected by solar activity. TEC is highly depended on local time, latitude, longitude, season, solar and geomagnetic conditions. The propagation of the signals from GNSS (Global Navigation Satellite System) throughout the ionosphere is strongly influenced by short- and long-term changes and ionospheric regular or irregular variations. <br>Long short-term memory network (LSTM) is a specific recurrent neural network architecture and is capable of learning time dependence in sequential problems and can successfully model ionosphere variability. As LSTM networks “memorize” long term correlations in a sequence, they can model complex sequences with various features, where solar radio flux at 10.7 cm and magnetic activity indices are taken into consideration to provide more accurate results. <br>Here, we propose a deep learning architecture to create regional TEC models around a station. The proposed model allows different solar and geomagnetic parameters to be inserted into the model as features. Our model has been evaluated under different solar and geomagnetic conditions. Also, the proposed model is tested for different time periods and seasonal variations and for varying geographic latitudes. </p>

scholarly journals Characterization of multi-scale ionospheric irregularities using ground-based and space-based GNSS observations

2021 ◽  
Vol 2 (1) ◽  
Author(s):  
YuXiang Peng ◽  
Wayne A Scales ◽  
Michael D Hartinger ◽  
Zhonghua Xu ◽  
Shane Coyle
Keyword(s):  
Formation Flying ◽  
Satellite System ◽  
Ionospheric Irregularities ◽  
Mission Design ◽  
Electron Content ◽  
Total Electron ◽  
Spatial And Temporal Scales ◽  
Multi Scale ◽  
Global Navigation Satellite ◽  
Gnss Observations

AbstractIonospheric irregularities can adversely affect the performance of Global Navigation Satellite System (GNSS). However, this opens the possibility of using GNSS as an effective ionospheric remote sensing tool. Despite ionospheric monitoring has been undertaken for decades, these irregularities in multiple spatial and temporal scales are still not fully understood. This paper reviews Virginia Tech’s recent studies on multi-scale ionospheric irregularities using ground-based and space-based GNSS observations. First, the relevant background of ionospheric irregularities and their impact on GNSS signals is reviewed. Next, three topics of ground-based observations of ionospheric irregularities for which GNSS and other ground-based techniques are used simultaneously are reviewed. Both passive and active measurements in high-latitude regions are covered. Modelling and observations in mid-latitude regions are considered as well. Emphasis is placed on the increased capability of assessing the multi-scale nature of ionospheric irregularities using other traditional techniques (e.g., radar, magnetometer, high frequency receivers) as well as GNSS observations (e.g., Total-Electron-Content or TEC, scintillation). Besides ground-based observations, recent advances in GNSS space-based ionospheric measurements are briefly reviewed. Finally, a new space-based ionospheric observation technique using GNSS-based spacecraft formation flying and a differential TEC method is demonstrated using the newly developed Virginia Tech Formation Flying Testbed (VTFFTB). Based on multi-constellation multi-band GNSS, the VTFFTB has been developed into a hardware-in-the-loop simulation testbed with external high-fidelity global ionospheric model(s) for 3-satellite formation flying, which can potentially be used for new multi-scale ionospheric measurement mission design.

scholarly journals Activities of daily living with bionic arm improved by combination training and latching filter in prosthesis control comparison

2021 ◽  
Vol 18 (1) ◽  
Author(s):  
Michael D. Paskett ◽  
Mark R. Brinton ◽  
Taylor C. Hansen ◽  
Jacob A. George ◽  
Tyler S. Davis ◽  
...  
Keyword(s):  
Neural Network ◽  
Kalman Filter ◽  
Activities Of Daily Living ◽  
Convolutional Neural Network ◽  
Daily Living ◽  
Complex Data ◽  
Multi Layer Perceptron ◽  
Experimental Conditions ◽  
Modified Kalman Filter ◽  
Prosthesis Control

Abstract Background Advanced prostheses can restore function and improve quality of life for individuals with amputations. Unfortunately, most commercial control strategies do not fully utilize the rich control information from residual nerves and musculature. Continuous decoders can provide more intuitive prosthesis control using multi-channel neural or electromyographic recordings. Three components influence continuous decoder performance: the data used to train the algorithm, the algorithm, and smoothing filters on the algorithm’s output. Individual groups often focus on a single decoder, so very few studies compare different decoders using otherwise similar experimental conditions. Methods We completed a two-phase, head-to-head comparison of 12 continuous decoders using activities of daily living. In phase one, we compared two training types and a smoothing filter with three algorithms (modified Kalman filter, multi-layer perceptron, and convolutional neural network) in a clothespin relocation task. We compared training types that included only individual digit and wrist movements vs. combination movements (e.g., simultaneous grasp and wrist flexion). We also compared raw vs. nonlinearly smoothed algorithm outputs. In phase two, we compared the three algorithms in fragile egg, zipping, pouring, and folding tasks using the combination training and smoothing found beneficial in phase one. In both phases, we collected objective, performance-based (e.g., success rate), and subjective, user-focused (e.g., preference) measures. Results Phase one showed that combination training improved prosthesis control accuracy and speed, and that the nonlinear smoothing improved accuracy but generally reduced speed. Phase one importantly showed simultaneous movements were used in the task, and that the modified Kalman filter and multi-layer perceptron predicted more simultaneous movements than the convolutional neural network. In phase two, user-focused metrics favored the convolutional neural network and modified Kalman filter, whereas performance-based metrics were generally similar among all algorithms. Conclusions These results confirm that state-of-the-art algorithms, whether linear or nonlinear in nature, functionally benefit from training on more complex data and from output smoothing. These studies will be used to select a decoder for a long-term take-home trial with implanted neuromyoelectric devices. Overall, clinical considerations may favor the mKF as it is similar in performance, faster to train, and computationally less expensive than neural networks.

scholarly journals Estimation and Analysis of GNSS Differential Code Biases (DCBs) Using a Multi-Spacing Software Receiver

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 443
Author(s):  
Ye Wang ◽  
Lin Zhao ◽  
Yang Gao
Keyword(s):  
Primary Source ◽  
Intermediate Frequency ◽  
Global Navigation Satellite Systems ◽  
Electron Content ◽  
Software Receiver ◽  
International Gnss Service ◽  
Total Electron ◽  
Satellite Systems ◽  
Differential Code Biases ◽  
Gnss Observations

In the use of global navigation satellite systems (GNSS) to monitor ionosphere variations by estimating total electron content (TEC), differential code biases (DCBs) in GNSS measurements are a primary source of errors. Satellite DCBs are currently estimated and broadcast to users by International GNSS Service (IGS) using a network of GNSS hardware receivers which are inside structure fixed. We propose an approach for satellite DCB estimation using a multi-spacing GNSS software receiver to analyze the influence of the correlator spacing on satellite DCB estimates and estimate satellite DCBs based on different correlator spacing observations from the software receiver. This software receiver-based approach is called multi-spacing DCB (MSDCB) estimation. In the software receiver approach, GNSS observations with different correlator spacings from intermediate frequency datasets can be generated. Since each correlator spacing allows the software receiver to output observations like a local GNSS receiver station, GNSS observations from different correlator spacings constitute a network of GNSS receivers, which makes it possible to use a single software receiver to estimate satellite DCBs. By comparing the MSDCBs to the IGS DCB products, the results show that the proposed correlator spacing flexible software receiver is able to predict satellite DCBs with increased flexibility and cost-effectiveness than the current hardware receiver-based DCB estimation approach.

scholarly journals Assessing the performance of a Northeast Asia Japan-cantered 3-D ionosphere specification technique during the 2015 St. Patrick’s day solar storm

2021 ◽  
Author(s):  
Nicholas Ssessanga ◽  
Mamoru Yamamoto ◽  
Susumu Saito
Keyword(s):  
Northeast Asia ◽  
Three Dimensional ◽  
Satellite System ◽  
Electron Content ◽  
Potential Candidate ◽  
Storm Events ◽  
Total Electron ◽  
Total Electron Content Data ◽  
Swarm Satellites ◽  
Solar Storm

Abstract This paper demonstrates and assesses the capability of the advanced three- dimensional (3-D) ionosphere tomography technique, during severe conditions. The study area is northeast Asia and quasi-Japan-centred. Reconstructions are based on Total electron content data from a dense ground-based global navigation satellite system receiver network and parameters from operational ionosondes. We used observations from ionosondes, Swarm satellites and radio occultation (RO) to assess the 3-D picture. Specifically, we focus on St. Patrick’s day solar storm (17–19 March 2015), the most intense in solar cycle 24. During this event, the energy ingested into the ionosphere resulted in Dst and Kp and reaching values ~-223 nT and 8, respectively, and the region of interest, the East Asian sector, was characterized by a ~ 60% reduction in electron densities. Results show that the reconstructed densities follow the physical dynamics previously discussed in earlier publications about storm events. Moreover, even when ionosonde data were not available, the technique could still provide a consistent picture of the ionosphere vertical structure. Furthermore, analyses show that there is a profound agreement between the RO profiles/in-situ densities and the reconstructions. Therefore, the technique is a potential candidate for applications that are sensitive to ionospheric corrections.

scholarly journals A Neural Network-Based TEC Model Capable of Reproducing Nighttime Winter Anomaly

2021 ◽  
Vol 13 (22) ◽  
pp. 4559
Author(s):  
Marjolijn Adolfs ◽  
Mohammed Mainul Hoque
Keyword(s):  
Neural Network ◽  
Solar Activity ◽  
Geomagnetic Latitude ◽  
Machine Learning Techniques ◽  
High Solar Activity ◽  
Electron Content ◽  
Solar Cycles ◽  
Total Electron ◽  
The Neural Network ◽  
Winter Anomaly

With the availability of fast computing machines, as well as the advancement of machine learning techniques and Big Data algorithms, the development of a more sophisticated total electron content (TEC) model featuring the Nighttime Winter Anomaly (NWA) and other effects is possible and is presented here. The NWA is visible in the Northern Hemisphere for the American sector and in the Southern Hemisphere for the Asian longitude sector under solar minimum conditions. During the NWA, the mean ionization level is found to be higher in the winter nights compared to the summer nights. The approach proposed here is a fully connected neural network (NN) model trained with Global Ionosphere Maps (GIMs) data from the last two solar cycles. The day of year, universal time, geographic longitude, geomagnetic latitude, solar zenith angle, and solar activity proxy, F10.7, were used as the input parameters for the model. The model was tested with independent TEC datasets from the years 2015 and 2020, representing high solar activity (HSA) and low solar activity (LSA) conditions. Our investigation shows that the root mean squared (RMS) deviations are in the order of 6 and 2.5 TEC units during HSA and LSA period, respectively. Additionally, NN model results were compared with another model, the Neustrelitz TEC Model (NTCM). We found that the neural network model outperformed the NTCM by approximately 1 TEC unit. More importantly, the NN model can reproduce the evolution of the NWA effect during low solar activity, whereas the NTCM model cannot reproduce such effect in the TEC variation.

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