Deep neural network‐based Wi‐Fi/pedestrian dead reckoning indoor positioning system using adaptive robust factor graph model
Deep neural network‐based Wi‐Fi/pedestrian dead reckoning indoor positioning system using adaptive robust factor graph model
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DOI：
10.1049/iet-rsn.2019.0260
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发表时间：
2020-01
期刊：
影响因子：
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通讯作者：
Yifan Wang;Zengke Li;Jingxiang Gao;Long Zhao
中科院分区：
文献类型：
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作者：
Yifan Wang;Zengke Li;Jingxiang Gao;Long Zhao
提出了一种基于深度神经网络（DNN）的Wi-Fi/行人航位推算（PDR）室内定位系统，该系统采用自适应鲁棒因子图模型，用于智能手机的室内定位。在Wi-Fi定位中，作者使用DNN在离线阶段从波动的Wi-Fi信号中提取鲁棒特征，并在在线定位中通过计算后验概率获得更准确的定位结果。加速度、陀螺仪和磁力计数据分别用于计算姿态角、步进频率和步长。接收到的Wi-Fi信号强度在复杂的室内环境中很容易受到影响，并且PDR误差会随着时间的推移而积累。提出一种自适应鲁棒调整因子图模型融合Wi-Fi和PDR的定位结果，克服了Wi-Fi和PDR误差随时间累积更新频率慢和粗差等缺点。当PDR缺失时，引入隐马尔可夫模型对未知点处的多个基于DNN的Wi-Fi定位估计进行平滑，以获得最优解。实验结果表明，该系统具有更强的鲁棒性，并具有更好的准确性，在不同的运动姿态（手持，悬挂，呼叫）。
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).