Intelligent vectorised architecture for performance enhancement of GNSS receivers in signal blocking situations

Abstract

Navigation in harsh environments mostly encounters many problems such as signal blocking due to a variety of obstacles around the receiver. Vectorised receiver has an architecture in which the channels can share the information. As a result, the strong channels aid the blocked one to reacquire the signal immediately after returning. However, if the output of a channel is undesirable, it may disrupt the operation of the system especially for the weak blocked channels. In this paper, an intelligent vectorised architecture is proposed to solve this problem. Three popular architectures including federated, adaptive, and vectorised are combined and then evidence theory is utilised to select the result with higher certainty. The experimental results in urban canyons show the improvements in signal availability and thermal noise performance.

Keywords:

• Federated receiver
• Vectorised receiver
• Adaptive tracking
• Evidence theory
• Signal blocking
• Software receiver

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by the Ministry of Education and Science of the Russian Federation.

Notes on contributors

A. Tabatabaei

A. Tabatabaei received his Ph.D. degree in Electronic Engineering from Iran University of Science and Technology (IUST) in 2012, Tehran, Iran. He is a lecturer and senior researcher at Samara State Aerospace University (SSAU), Samara, Russia. His research interests include GNSS software receivers and GNSS interference mitigation methods.

Z. Koohi

Z. Koohi received her B.S. and M.S. degrees in Electronic Engineering from Department of Electrical Engineering, Iran University of Science and Technology (IUST), Tehran, Iran in 2016 and 2019 respectively. Her research interests include Global Positioning Systems, Artificial Intelligent System and Digital Signal Processing.

M. R. Mosavi

M. R. Mosavi received his B.S., M.S., and Ph.D. degrees in Electronic Engineering from Iran University of Science and Technology (IUST), Tehran, Iran in 1997, 1998, and 2004, respectively. He is currently faculty member (full professor) of the Department of Electrical Engineering of IUST. He is the author of more than 400 scientific publications in journals and international conferences in addition to 11 academic books. His research interests include circuits and systems design. He is also editor-in-chief of Iranian Journal of Marine Technology and editorial board member of Iranian Journal of Electrical and Electronic Engineering.

Z. Tabatabaei

Z. Tabatabaei received her B.S. and M.S. degrees in Electronic Engineering from respectively Bu-Ali Sina University (BASU) in 2017 and Hamedan University of Technology (HUT) in 2019, Hamedan, Iran. She is currently a Ph.D. student at the Institute of Research and Innovation in Bioengineering, Universitat Politècnica de València (UPV), Valencia, Spain. She is also working in Tyris Software company (Tyris.Ai). Her research interests include Artificial Intelligence and Machine Learning.