An LSTM Approach for Modelling Error of Smartphone-reported GNSS Location Under Mixed LOS/NLOS Environments

Modelling error of smartphone-reported Global Navigation Satellite System (GNSS) locations plays an important role in urban navigation under mixed LOS/NLOS environments. In the case of pedestrian navigation, the performance of GNSS error modeling significantly affects the precision of final multi-source fusion. In this work, a novel Long Short-Term Memory (LSTM) network is developed for error modeling of smartphone-reported GNSS locations combined with the detected human motion information. The LSTM network is applied to adaptively combine multi-level observations provided by GNSS and built-in sensors-based location sources under a specific time period instead of considering only adjacent timestamps. The motion features extracted from multi-level observations is then modeled as the input vector of LSTM for training and prediction purposes, and the predicted errors under two axis in the n-frame are finally modeled as the error covariance matrix and applied in the multi-sources fusion structure. The comprehensive experiments indicate the effectivity and significant improvement for integrated localization after GNSS error modeling.