Integration of Artificial Neural Network and the Optimal GNSS Satellites’ Configuration for Improving GNSS Positioning Techniques (A Case Study in Egypt)

Abstract

Nowadays, theglobal navigation satellite system (GNSS) positioning techniques based on the International GNSS Service (IGS) products are extensively used for various precise applications. However, specific conditions such as the dual-frequency observations and the final IGS products are required. Consequently, the absence of the final IGS data and using single-frequency observations will degrade these techniques’ accuracy. In this paper, two algorithms through two separated stages are formulated for improving the single-frequency GNSS observations by using one GNSS receiver based on the broadcast ephemerides in real time or close to real time. The first algorithm represents the preparation stage for the second one. It classifies the observations by separating the optimal values of position dilution of precision (PDOP) and the number of satellites (NOS), as well as the corresponding values of coordinates. The second stage includes an algorithm based on the artificial neural network (ANN) approach, which is set at the ANN variables that produce the best precision through the applied tests at the present study. Binary numbers, log sigmoid-Purelin, cascade forward net, and one hidden layer with a size of 10 neurons are the optimal variables of ANN inputs format, transfer functions constellations, feedforward net type, and the number of hidden layers (NHL) and its size, respectively. The simulation results show that the designed algorithms produce a significant improvement in the horizontal and vertical components. Lastly, an evaluation stage is performed in the case of dual-frequency observations by using broadcast ephemerides. The simulation outputs indicate that the precision at applying the proposed integration is completely enhanced compared with the outputs of IGS final data.

A Proposed Merging Methods of Digital Elevation Model Based on Artificial Neural Network and Interpolation Techniques for Improved Accuracy

“…Consequently, the number of initializations was set at 100; this iterated automatically. One hidden layer with ten neurons was selected, according to the recommendation of Alemam, Yong, and Mohammed (2022). During the development of this ANN algorithm, the results indicated that the linear activation function (pure-line), in the hidden and output layers, is suitable for this network and data compared to the other activation functions.…”

“…Overall, the ANN reached the best results with additional GNSS points at the border. This is because the ANN simulation performs best with a high volume of data (Alemam et al 2022). Therefore, the ANN is preferred as its cost-effective.…”

Geodetic Data Processing Using Soft Computing Techniques

“…In response to these challenges, researchers have begun to explore alternative approaches to geodetic data processing, with soft computing techniques emerging as a promising option. Soft computing is an interdisciplinary field that encompasses several computational methodologies, including artificial neural networks (ANNs) [3][4][5], fuzzy logic, and evolutionary algorithms (EAs). These techniques are characterized by their ability to handle imprecise, uncertain, and incomplete information, making them well-suited for addressing the challenges associated with geodetic data processing.…”