Open AccessDeep neural network‐based Wi‐Fi/pedestrian dead reckoning indoor positioning system using adaptive robust factor graph model
Author(s) -
Wang Yifan,
Li Zengke,
Gao Jingxiang,
Zhao Long
Publication year - 2020
Publication title -
iet radar, sonar and navigation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.489
H-Index - 82
eISSN - 1751-8792
pISSN - 1751-8784
DOI - 10.1049/iet-rsn.2019.0260
Subject(s) - dead reckoning , computer science , factor graph , gyroscope , real time computing , artificial intelligence , hidden markov model , indoor positioning system , graph , computer vision , accelerometer , algorithm , global positioning system , engineering , telecommunications , decoding methods , theoretical computer science , aerospace engineering , operating system
A deep neural network (DNN)‐based Wi‐Fi/pedestrian dead reckoning (PDR) indoor positioning system using an adaptive robust factor‐graph model is proposed in this study for the indoor positioning of smartphones. In Wi‐Fi positioning, the authors use a DNN to extract robust features from fluctuant Wi‐Fi signals in the off‐line phase, and obtain more accurate positioning results by computing posterior probabilities in online positioning. Acceleration, gyroscope, and magnetometer data are used to calculate attitude angle, step frequency, and step length, respectively. Received Wi‐Fi signal strength is susceptible in complex indoor environments, and PDR errors accumulate over time. A factor‐graph model with adaptive robust adjustment is proposed to fuse the positioning results of Wi‐Fi and PDR, and it overcomes such shortcomings as slow update frequency and gross errors of Wi‐Fi and PDR errors accumulated over time, respectively. When the absence of PDR occurs, hidden Markov model is introduced to smooth multiple DNN‐based Wi‐Fi positioning estimates at the unknown point to obtain the optimal solution. Experimental results show that the proposed system is more robust and has better accuracy under different motion gestures (held‐in‐hand, dangling, and calling).