Deep Neural Network Approach to GNSS Signal Acquisition

Parisa Borhani-Darian and Pau Closas

Abstract: This paper investigates the use of data-driven models, popular in the machine learning literature, as an alternative to well-engineered signal processing blocks used in state-of-theart GNSS receivers. Acknowledging that the latter are optimally designed and extensively tested, it is also agreed that when the nominal models do not hold the performance of the receiver might degrade. Particularly, we investigate the use of datadriven models in the signal acquisition stage of the receiver by addressing a classification problem from Cross Ambiguity Function (CAF) delay/Doppler maps. A discussion on the training of such models and future perspectives is provided. The detection results in nominal situations are then compared to the theoretical bound in the receiver operating characteristic (ROC) plots.
Published in: 2020 IEEE/ION Position, Location and Navigation Symposium (PLANS)
April 20 - 23, 2020
Hilton Portland Downtown
Portland, Oregon
Pages: 1214 - 1223
Cite this article: Borhani-Darian, Parisa, Closas, Pau, "Deep Neural Network Approach to GNSS Signal Acquisition," 2020 IEEE/ION Position, Location and Navigation Symposium (PLANS), Portland, Oregon, April 2020, pp. 1214-1223.
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