Deep learning of total electron content

Abstract

One of the most notable errors in the global navigation satellite system (GNSS) is the ionospheric delay due to the total electron content (TEC). TEC is the number of electrons in the ionosphere in the signal path from the satellite to the receiver, which fluctuates with time and location. This error is one of the major problems in single-frequency (SF) GPS receivers. One way to eliminate this error is to use dual-frequency. Users of SF receivers should either use estimation models or local models to reduce this error. In this study, deep learning of artificial neural networks (ANN) was used to estimate TEC for SF users. For this purpose, the ionosphere as a single-layer model (assuming that all free electrons in the ionosphere are in this thin layer) is locally modeled by the code observation method. Linear combination has been used by selecting 24 permanent GNSS stations in the northwest of Iran. TEC was modeled independently of the geometry between the satellite and the receiver, called L4. This modeling was used to train the error ANN with two 5-day periods of high and low solar and geomagnetic activity range with a hyperbolic tangential sigmoid activation function. The results show that the proposed method is capable of eliminating ionosphere error with an average accuracy of 90%. The international reference ionosphere 2016 (IRI2016) is used for the verification, which has a 96% significance correlation with estimated TEC.

Investigating the Efficiency of Deep Learning Methods in Estimating GPS Geodetic Velocity

“…This layer performs an operation called convolution and is created to receive and process data. The CNN is composed of multilayer perceptrons and one or more convolutional layers that can be fully connected or integrated (Sorkhabi, 2021). This method is very efficient and is one of the most common methods in various applications of computer vision.…”

“…In each CNN there are two steps for training: feedforward stage and backpropagation stage. In the first stage, the input signal is fed to the network and this action is nothing but multiplying the point between the input and the parameters of each neuron and finally applying convolution operation in each layer; The network output is then calculated (Sorkhabi, 2021). Here, the network parameters are adjusted, or in other words, the network are trained, the output result is used to calculate the amount of network error.…”