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    Volume 1, Issue 1, Chinese Journal of Information Fusion
    Volume 1, Issue 1, 2024
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    You He
    You He
    Tsinghua University, China
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    Chinese Journal of Information Fusion, 2024, Volume 1, Issue 1: 50-62

    Free to Read | Research Article | 08 June 2024
    GPS Tracking Based on Stacked-Serial LSTM Network
    by
    IECE Author Xuebo Jin 1 *
    IECE Author Songzheng Liu 1
    IECE Author Jianlei Kong 1
    IECE Author Yuting Bai 1
    IECE Author Tingli Su 1
    IECE Author Huijun Ma 1
    1 School of Computer Science and Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, China
    * Corresponding Author: Xuebo Jin, [email protected]
    DOI: https://doi.org/10.62762/CJIF.2024.361889
    Received: 22 March 2024, Accepted: 01 June 2024, Published: 08 June 2024  
    Cited by: 2  (Source: Web of Science) , 3  (Source: Google Scholar)
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    Abstract
    Maneuvering target tracking is widely used in unmanned vehicles, missile navigation, underwater ships, etc. Due to the uncertainty of the moving characteristics of maneuvering targets and the low sensor measurement accuracy, trajectory tracking has always been an open research problem and challenging work. This paper proposes a trajectory estimation method based on LSTM neural network for uncertain motion characteristics. The network consists of two LSTM networks with stacked serial relationships, one of which is used to predict the movement dynamics, and the other is used to update the track's state. Compared with the classical Kalman filter based on the maneuver model, the method proposed here does not need to model the motion characteristics and sensor characteristics. It can achieve high-performance tracking by learning historical data dynamics and sensor characteristics. Experimental results show that this method can effectively improve the trajectory estimation performance when the target motion is unknown and uncertain.
    Graphical Abstract
    GPS Tracking Based on Stacked-Serial LSTM Network
    Keywords
    Trajectory Estimation
    Recurrent Neural Network
    GPS
    Filtering Algorithm
    LSTM
    Stacked Serial Structure
    Funding
    This work was supported without any funding.
    Cite This Article
    APA Style
    Jin, X., Liu, S., Kong, J., Bai, Y., Su, T., & Ma, H. (2024). GPS Tracking Based on Stacked-Serial LSTM Network. Chinese Journal of Information Fusion, 1(1), 50–62. https://doi.org/10.62762/CJIF.2024.361889
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    Chinese Journal of Information Fusion

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