Abstract: Due to the degraded performance of pedestrian navigation system when foot-mounted IMU is out of range during vigo-rous activities or collisions,a novel pedestrian navigation method is proposed based on construction of virtual inertial measurement unit(VIMU) assisted by gait classification.Attention-based convolutional neural network(CNN) is introduced to classify the common gaits of pedestrian.Then the inertial data from pedestrian's thigh and foot is collected synchronously via actual IMUs as training and testing samples.For different gaits,the corresponding ResNet-gated recurrent unit(Resnet-GRU) hybrid neural network models are built.According to these models,virtual foot-mounted IMU is constructed for positioning in case of actual foot-mounted IMU overrange.Experiments show that,the proposed method brings enhanced performance of pedestrian navigation system based on zero velocity update when the foot motion of pedestrian is violent,which makes the navigation system more adaptable in complex and unknown terrains.The positioning error during comprehensive gait is about 1.43% of the total walking distance,which satisfies the accuracy requirement of military and civilian applications.

Key words: Attention-based convolu-tional neural network, Gait classification, Pedestrian navigation, ResNet-gated recurrent unit neural network, Virtual inertial measurement unit construction