Application of long short term memory neural networks for GPS Satellite clock bias prediction

• Autorzy: Gnyś Piotr , Przestrzelski Paweł

Identyfikatory

DOI

10.34808/tq2021/25.4/a

• Języki publikacji: EN

Abstrakty

EN

Satellite-based localization systems like GPS or Galileo are one of the most com-monly used tools in outdoor navigation. While for most applications, like car navigation orhiking, the level of precision provided by commercial solutions is satisfactory it is not alwaysthe case for mobile robots. In the case of long-time autonomy and robots that operate in re-mote areas battery usage and access to synchronization data becomes a problem. In this paper,a solution providing a real-time onboard clock synchronization is presented. Results achievedare better than the current state-of-the-art solution in real-time clock bias prediction for mostsatellites.

Słowa kluczowe

EN

neural networks; LSTM; time series prediction; clock bias; GNSS; machine learning

PL

sieci neuronowe; LSTM; przewidywanie szeregów czasowych; odchylenie zegara; GNSS; uczenie maszynowe

• Wydawca: Politechnika Gdańska
• Czasopismo: TASK Quarterly : scientific bulletin of Academic Computer Centre in Gdansk
• Rocznik: 2021
• Tom: Vol. 25, No 4
• Strony: 381--395
• Opis fizyczny: Bibliogr. 21 poz., rys., tab.

Twórcy

autor

Gnyś Piotr

• Department of Computer Science, Polish-Japanese Academy of Information Technology, Koszykowa 86, 02-008 Warsaw, Poland

autor

Przestrzelski Paweł

• Department of Computer Science, Polish-Japanese Academy of Information Technology, Koszykowa 86, 02-008 Warsaw, Poland

Bibliografia

• [1] Cabrera-Gámez J et al. 2013 An Embedded Low-Power Control System for Autonomous Sailboats, Robotic Sailing doi: https://doi.org/10.1007/978-3-319-02276-5 6
• [2] Blewitt G 1997 Basics of the GPS Technique: Observation Equations, Geodetic Applications of GPS 1–46
• [3] Doberstein D 2012 Fundamentals of GPS receivers: A hardware approach, Springer New York doi: https://doi.org/10.1007/978-1-4614-0409-5
• [4] Enge P 2011 Global Positioning System: Signals, Measurements, and Performance - Revised Second Edition, International Journal of Wireless Information Networks
• [5] Riley W J 2007 Handbook of Frequency Stability Analysis, NIST Special Publication 1065 (31 Issue 1)
• [6] Kouba J 2009 A Guide to using international GNSS Service ( IGS ) Products, Geodetic Survey Division Natural Resources Canada Ottawa
• [7] Abiodun O I et al. 2019 Comprehensive Review of Artificial Neural Network Applications to Pattern Recognition, IEEE Access 7 158820-158846 doi: 10.1109/ACCESS.2019.2945545
• [8] Faraway J and Chatfield C 2019 Time series forecasting with neural networks: a comparative study using the airline data, Journal of the Royal Statistical Society: Series C (Applied Statistics) 47 (2) 231–250 doi: https://doi.org/10.1111/1467-9876.00109
• [9] Khan A et al. A Survey of the Recent Architectures of Deep Convolutional Neural Networks http://arxiv.org/abs/1901.06032
• [10] Miller A S 1993 Vistas in Astronomy, doi: https://doi.org/10.1016/0083-6656(93) 90118-4, 36 141
• [11] Kim H U and Bae T S 2019 Deep Learning-Based GNSS Network-Based Real-Time Kinematic Improvement for Autonomous Ground Vehicle Navigation, Journal of Sensors 1–8 doi: https://doi.org/10.1155/2019/3737265
• [12] Orus P R 2019 Using TensorFlow-based Neural Network to estimate GNSS single frequency ionospheric delay (IONONet), Advances in Space Research 63 (5) 1607–1618 doi: https://doi.org/10.1016/j.asr.2018.11.011
• [13] Wei J et al The Satellite Selection Algorithm of GNSS Based on Neural Network, 115–123Overview
• [14] Indriyatmoko A et al. 2008 Artificial neural networks for predicting DGPS carrier phase and pseudorange correction, GPS Solutions 12 (4) 237–247 doi: https://doi.org/10.1007/s10291-008-0088-x
• [15] Wang Y et al 2017 Improving prediction performance of GPS satellite clock bias based on wavelet neural network, GPS Solutions 21 (2) 523–534 doi: https://doi.org/10.1007/s10291-016-0543-z
• [16] McCulloch W S and Pitts W 1943 A logical calculus of the ideas immanent in nervous activity, The Bulletin of Mathematical Biophysics doi: https://doi.org/10.1007/BF02478259
• [17] Hochreiter S and Schmidhuber J 1997 Long Short-Term Memory, Neural Computation doi: https://doi.org/10.1162/neco.1997.9.8.1735
• [18] Chollet F, Deep Learning Phyton
• [19] Hinton G E et al 2012 Lecture 6a- overview of mini-batch gradient descent, COURSERA: Neural Networks for Machine Learning
• [20] Kingma D P and Ba J L 2015 Adam: A method for stochastic optimization, 3rd International Conference on Learning Representations, ICLR 2015-Conference Track Proceedings
• [21] Vallado D A and Crawford P 2008 SGP4 orbit determination, AIAA/AAS Astrodynamics Specialist Conference and Exhibit doi: https://doi.org/10.1007/978-3-662-50370-6 6