The pedestrian inertial navigation method based on deep learning has become a major research focus in recent years due to its high adaptability.However,existing approaches do not fully consider the temporal characteristics of the inertial data and lack the ability to fit temporal data.To restrain the error divergence in learning-based micro-inertial navigation systems,a pedestrian inertial navigation algorithm with a learnable time-series state space mode is established.Unlike traditional pedestrian dead reckoning(PDR)algorithms,the proposed algorithm does not rely on the traditional pedestrian inertial navigation framework.Instead,a selective two-layer bi-directional state space model is used to temporally model the implicit inertial feature vectors after feature encoding,and the motion displacement and the uncertainty are estimated.Furthermore,the neural network estimation results are fused by an extended Kalman filter in order to mitigate the error drift.Experiments conducted on wearable pedestrian navigation devices show that the proposed method improves positioning accuracy,effectively suppresses error drift in the inertial system,and achieves reliable pedestrian navigation.Compared to tight learned inertial odometry(TLIO),the absolute trajectory error and displacement drift rate are reduced by 32.35％and 41.27％respectively.
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