PositionNet: CNN-based GNSS positioning in urban areas with residual maps.

Multipath and non-light-of-sight (NLOS) reception greatly deteriorate the GNSS positioning accuracy in urban areas. Currently, most of the attempts using machine learning in this area focus on signal status prediction (LOS, multipath, NLOS). This paper exploits the capability of deep learning in multipath/NLOS mitigation. A new input feature, the single-differenced residual map is proposed, which has a high correlation with the user location and is very effective in multipath/NLOS mitigation. Combining the domain knowledge in GNSS, features of residual maps from different satellites are extracted by the proposed network and generate the heat map to indicate the user location. The proposed network can significantly improve the positioning accuracy of 84% of the epochs in the dense urban to 5-meter level. In addition, our network has a superior generalization ability, reducing the error of 90% of the epochs to 7 m level in a new scenario. • A deep learning network structure to directly estimate urban GNSS positioning. • A new GNSS feature, single-differenced residual maps, is proposed to improve the positioning accuracy of the neural network. • The proposed network is validated and tested using real GNSS raw data collected in urban canyons at Hong Kong. [ABSTRACT FROM AUTHOR]