P2Net: A Two-Stage Personalized Pedestrian Dead Reckoning Based on Neural Networks
Qianqian Du, Zihang Wang, Yujin Kuang, Yiqing Yao, Qingyue Cao, Yuan Yang · IEEE Sensors Journal · 2024
Pedestrian dead reckoning (PDR) uses built-in sensors in smartphones to track user positions, offering both versatility and portability. However, diversities among individuals and their behavior patterns decrease positioning accuracy. In this study, a two-stage personalized pedestrian dead reckoning based on neural networks (P2Net) is proposed. The first stage employs a human activity and smartphone location recognition (HSR) module, integrating a convolutional neural network with focal loss (FLCNN) to recognize nine modes, which constrains the subsequent optimized PDR procedure. In the second stage, a hybrid feature temporal attention network (HFTAN) is constructed to achieve generalized step length estimation across individual diversities. Temporal features via temporal convolutional network (TCN) and physical features are combined to generate hybrid features with time-series modeling capability and interpretability, which are then fed into a bi-directional long short-term memory (BiLSTM) model with a feature attention mechanism to estimate pedestrian step length. Experimental results demonstrate that P2Net achieves a class average accuracy of 92.75% for nine modes, and the total traveled distance error (TTDE) under various states is within 10%, outperforming other mentioned positioning methods.