中国科学院计算技术研究所机构知识库

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限定条件	作者：Zhao, Fang    第一作者

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	Res-EMSA: Adaptive Adjustment of Innovation Based on Efficient Multihead Self-Attention in GNSS/INS Tightly Integrated Navigation System 期刊论文 IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, 2024, 卷号: 21, 页码: 5 作者:  Xu, Hongfu;  Luo, Haiyong;  Wu, Zijian;  Zhao, Fang 收藏  |  浏览/下载：2/0  |  提交时间：2024/05/20 Technological innovation  Global navigation satellite system  Navigation  Feature extraction  Adaptation models  Measurement uncertainty  Kalman filters  Deep learning  extended Kalman filter (EKF)  innovation matrix  integrated navigation
	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
	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
	Pedestrian Walking Distance Estimation Based on Smartphone Mode Recognition 期刊论文 REMOTE SENSING, 2019, 卷号: 11, 期号: 9, 页码: 23 作者:  Wang, Qu;  Ye, Langlang;  Luo, Haiyong;  Men, Aidong;  Zhao, Fang;  Ou, Changhai 收藏  |  浏览/下载：76/0  |  提交时间：2019/08/16 indoor positioning  machine learning  pedestrian dead reckoning  stride length estimation  smartphone mode recognition