GNSS Cycle Slip Detection and Estimation Using LSTM-based Autoencoder Model.
Ragheb, A., Zekry, A., Elhabiby, M. (2021). GNSS Cycle Slip Detection and Estimation Using LSTM-based Autoencoder Model.. EKB Journal Management System, 46(2), 31-40. doi: 10.21608/bfemu.2021.195739
Ahmed E. Ragheb; Ahmed Zekry; Mohamed Elhabiby. "GNSS Cycle Slip Detection and Estimation Using LSTM-based Autoencoder Model.". EKB Journal Management System, 46, 2, 2021, 31-40. doi: 10.21608/bfemu.2021.195739
Ragheb, A., Zekry, A., Elhabiby, M. (2021). 'GNSS Cycle Slip Detection and Estimation Using LSTM-based Autoencoder Model.', EKB Journal Management System, 46(2), pp. 31-40. doi: 10.21608/bfemu.2021.195739
Ragheb, A., Zekry, A., Elhabiby, M. GNSS Cycle Slip Detection and Estimation Using LSTM-based Autoencoder Model.. EKB Journal Management System, 2021; 46(2): 31-40. doi: 10.21608/bfemu.2021.195739

GNSS Cycle Slip Detection and Estimation Using LSTM-based Autoencoder Model.

MEJ- Mansoura Engineering Journal
Article 15, Volume 46, Issue 2, June 2021, Page 31-40  XML PDF (1.35 MB)
Document Type: Research Studies
DOI: 10.21608/bfemu.2021.195739
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Authors
Ahmed E. Ragheb1; Ahmed Zekry2; Mohamed Elhabiby email 3
1Associate professor of Surveying and Geodesy., Public Works Department., Ain Shams University., Faculty of Engineering., Cairo., Egypt.
2Department of Electrical and Computer Engineering, Queen’s University, Kingston, Canada
3Associate professor., Public Works Departmen.t, Faculty of Engineering., Ain Shams University
Abstract
Global Navigation Satellite Systems (GNSSs) are used in many navigation and positioning applications. Unfortunately, a GNSS signal may suffer from some errors, such as cycle slips, which deteriorate the positioning solution. A cycle slip is defined as a sudden jump by an integer number of cycles in the GNSS carrier phase observations. Signal blockage or/and high troposphere activities are the most common causes for GNSSs’ cycle slips. Therefore, cycle slips should be detected and corrected to determine reliable positioning estimations. A new approach for cycle slip detection and repair is proposed based on a master-rover phase-difference with a deep Long Short-Term Memory (LSTM) neural network model; our SlipNet model can classify defective data where a cycle slip has occurred and then predict the exact epoch where the cycle slip(s) occurred. The proposed SlipNet network would be the first end-to-end learning framework to solve the integer ambiguity problem in GNSS measurements with high performance results, %99.7 detection and localization accuracy, and 0.045 MAE for slip estimation and recovery. These results are on par with the latest classical cycle slip detection methods of cycle slip detection and correction.
Keywords
GNSS; Cycle Slip; Slip Net; LSTM; Autoencoder
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