A Finite State Machine Aided Pedestrian Navigation State Matching Algorithm
FANG Zhixiang, Hao Luo, LI Ling · DOAJ (DOAJ: Directory of Open Access Journals) · 2017
The automatic identification of pedestrian's navigation state is a difficult problem in pedestrian navigation research. It is important to improve the precision feedback and navigation performance of pedestrian navigation services, and few researches have been done in this field. This paper proposes a pedestrian navigation state matching algorithm based on finite state machine (FSM). The main idea of this method is to identify the pedestrian navigation state on the basis of recognizing pedestrian's actions. The pedestrian's action characteristics are recognized by using multiple sensor data collected by Google glass and mobile phone. Then, the pedestrian navigation states are divided into familiar, unfamiliar and lost state. The state transition model is established according to the FSM theory, and the pedestrian navigation state matching algorithm based on the model is designed. Finally, this algorithm is implemented, and experiments are conducted to validate its effectiveness. Experimental results show that the proposed algorithm can reach a good precision of recognizing the state transitions during pedestrian navigation process, and especially the accuracy of recognizing lost state achieves 90%.