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    • wangyeqing@st.btbu.edu.cn
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    Yeqing Wang
    School of Computer Science and Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, China
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    Free Access | Research Article | 29 May 2024 | Cited: 4
    Parameter Adaptive Non-Model-Based State Estimation Combining Attention Mechanism and LSTM
    by
    Xuebo Jin
    ,
    Tianxiao Sun
    ,
    Wei Chen
    ,
    Huijun Ma
    ,
    Yeqing Wang
    ,
    Yusen Zheng
    IECE Transactions on Intelligent Systematics | Volume 1, Issue 1: 40-48, 2024 | DOI:10.62762/TIS.2024.137329
    Abstract
    Nowadays, state estimation is widely used in fields such as autonomous driving and drone navigation. However, in practical applications, it is difficult to obtain accurate target motion models and noise covariance.This leads to a decrease in the estimation accuracy of traditional Kalman filters. To address this issue, this paper proposes an adaptive model free state estimation method based on attention parameter learning module. This method combines Transformer's encoder with Long Short Term Memory Network (LSTM), and obtains the system's operational characteristics through offline learning of measurement data without modeling the system dynamics and measurement characteristics. In addition,... More >
    Graphical Abstract
    Parameter Adaptive Non-Model-Based State Estimation Combining Attention Mechanism and LSTM
    Free Access | Research Article | Feature Paper | 26 May 2024
    Pedestrian Trajectory Reconstruction for Indoor Movement Based on Foot-Mounted IMU
    by
    Yeqing Wang
    ,
    Binbin Wang
    IECE Transactions on Intelligent Systematics | Volume 1, Issue 1: 19-29, 2024 | DOI:10.62762/TIS.2024.136995
    Abstract
    A pedestrian navigation system (PNS) that only relies on the foot-mounted IMU is useful for various applications, especially under some severe conditions, such as tracking of firefighters and miners. Due to the complexity of the indoor environment, signal occlusion problems could lead to the failure of certain positioning methods. In complex environments such as fire rescue and emergency rescue, the barometric altimeter fails because of the influence of air pressure and temperature. This paper used an improved zero velocity detection algorithm to improve the accuracy of gait detection. Then, combine the Kalman filter with the zero velocity update algorithm to recognize gait accurately and ta... More >
    Graphical Abstract
    Pedestrian Trajectory Reconstruction for Indoor Movement Based on Foot-Mounted IMU
    Free Access | Research Article | 25 May 2024 | Cited: 2
    Deep Prediction Network Based on Covariance Intersection Fusion for Sensor Data
    by
    Hanchi Ren
    ,
    Yeqing Wang
    ,
    Huijun Ma
    IECE Transactions on Intelligent Systematics | Volume 1, Issue 1: 10-18, 2024 | DOI:10.62762/TIS.2024.136898
    Abstract
    To predict future trends based on the data from sensors is an important technology for many applications, such as the Internet of Things, smart cities, etc. Based on the predicted results, further decisions and system controls can be made. Raw sensor data sets are often complex non-linear data with noise, which results in the difficulty of accurate prediction. This paper proposes a distributed deep prediction network based on a covariance intersection (CI) fusion algorithm in which the deep learning networks, such as long-term and short-term memory networks (LSTM) and gated recurrent unit networks (GRU) are fused by CI fusion algorithm to effectively develop the performance of prediction. Mo... More >
    Graphical Abstract
    Deep Prediction Network Based on Covariance Intersection Fusion for Sensor Data