Leveraging Machine Learning to Mitigate Multipath in a GNSS pureL5 Receiver

Mahdi MaarefLionel Garin, and Paul McBurney

oneNav, Inc., Palo Alto, California

Abstract

A Machine Learning (ML)-based framework for navigating with global navigation satellite system (GNSS) signals in urban environments is developed. This framework aims to incorporate a pureL5 navigation system to obviate the requirement for a dual frequency RF front-end. To this end, first, this paper quantifies the performance of a pureL5 receiver in static and dynamic heavy multipath signal environments. Then, a deep neural network (DNN)-based methodology to leverage ML to mitigate multipath is presented. The performance of the proposed framework is analyzed. Experimental results for a dynamic receiver navigating in a deep urban environment show that the proposed framework reduces the 95% horizontal confidence level from 44.0 m to 18.8 m. It is also shown that the proposed framework is able to reduce the standard deviation of the pseudorange error from 11.22 m to 5.34 m.

Interference Immunity of pureL5 GNSS Receivers in Mobile Devices

Anthony Tsangaropoulos and Pradeep Gubbi Prakash

oneNav, Inc., Palo Alto, California

Abstract

oneNav’s unique design for a pureL5 (i.e. L5/E5/B2) GNSS receiver offers significant advantages compared to conventional L1-band or dual-frequency L1+L5 solutions. This is especially true in mobile device platforms. Beyond the significant cost and area benefits, increased accuracy and immunity to multipath, a pureL5 receiver offers clear advantages in terms of interference immunity to 2/3/4/5G and WiFi/Bluetooth onboard emissions. A detailed analysis of interference scenarios in mobile phones is presented. Harmonic and intermodulation products are considered for on board transmitters operating on present and future cellular modem bands in intraband LTE carrier aggregation (CA) and LTE-NR dual connectivity (ENDC) configurations and with 2.4GHz and 5GHz connectivity bands.

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