Session F6: GNSS Robustness to Vulnerabilities 2

Neural Network-Based GNSS Code Measurement De-Weighting for Multipath Mitigation
Haoqing Li and Kyle O’Keefe, Department of Geomatics Engineering, University of Calgary
Alternate Number 1

Peer Reviewed

This paper proposes a multi-layer perceptron (MLP)-based artificial neural network (ANN) to estimate the variance of code measurements over time for multipath de-weighting purposes. Particularly, we aim to estimate the variance of a short window of code measurements using an MLP and use the output to de-weight measurements affected by multipath. The proposed solution can be widely used in various positioning techniques. The proposed method is trained and tested using a suburban land vehicle data set that includes segments of urban canyon environments. Positions are estimated using the standard SPP least-squares solution with a conventional RAIM and FDE approach. Furthermore, the proposed method is compared with an expert variance estimation model for multipath de-weighting and the standard elevation-dependent variance estimation model. We highlight that the currently trained ANN holds results in smaller positioning error than that obtained using the standard elevation-dependent variance model under multipath influence and it can correctly identify some multipath events where the expert model fails.