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Towards Predicting the Measurement Noise Covariance with a Transformer and Residual Denoising Autoencoder for GNSS/INS Tightly-Coupled Integrated Navigation 期刊论文
REMOTE SENSING, 2022, 卷号: 14, 期号: 7, 页码: 22
作者:  Xu, Hongfu;  Luo, Haiyong;  Wu, Zijian;  Wu, Fan;  Bao, Linfeng;  Zhao, Fang
收藏  |  浏览/下载：19/0  |  提交时间：2022/12/07
tightly coupled integrated navigation  measurement noise estimation  transformer  adaptive Kalman filtering  
Particle Filter Reinforcement via Context-Sensing for Smartphone-Based Pedestrian Dead Reckoning 期刊论文
IEEE COMMUNICATIONS LETTERS, 2021, 卷号: 25, 期号: 9, 页码: 3144-3148
作者:  Shao, Wenhua;  Zhao, Fang;  Luo, Haiyong;  Tian, Hui;  Li, Jiaxin;  Crivello, Antonino
收藏  |  浏览/下载：41/0  |  提交时间：2021/12/01
Particle filters  Neural networks  Reinforcement learning  Mathematical model  Particle measurements  Estimation  Atmospheric measurements  Indoor location tracking  particle filter  pedestrian dead reckoning  reinforcement learning  smartphone-based navigation  
RL-AKF: An Adaptive Kalman Filter Navigation Algorithm Based on Reinforcement Learning for Ground Vehicles 期刊论文
REMOTE SENSING, 2020, 卷号: 12, 期号: 11, 页码: 25
作者:  Gao, Xile;  Luo, Haiyong;  Ning, Bokun;  Zhao, Fang;  Bao, Linfeng;  Gong, Yilin;  Xiao, Yimin;  Jiang, Jinguang
收藏  |  浏览/下载：57/0  |  提交时间：2020/12/10
integrated navigation  Kalman filter  process noise covariance estimation  reinforcement learning  deep deterministic policy gradient  
Adaptive Kalman filtering-based pedestrian navigation algorithm for smartphones 期刊论文
INTERNATIONAL JOURNAL OF ADVANCED ROBOTIC SYSTEMS, 2020, 卷号: 17, 期号: 3, 页码: 14
作者:  Yu, Chen;  Luo, Haiyong;  Fang, Zhao;  Qu, Wang;  Shao, Wenhua
收藏  |  浏览/下载：46/0  |  提交时间：2020/12/10
Pedestrian navigation  error model system  inertial sensors integration  magnetic field  heading estimation