12th EAI International Conference on Mobile Multimedia Communications, Mobimedia 2019, 29th - 30th Jun 2019, Weihai, China

Research Article

Adaptive fusion localization mechanism towards TDoA and IMU data with LSTM correction Method

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  • @INPROCEEDINGS{10.4108/eai.29-6-2019.2282992,
        author={Zhiyuan  Liu and Liang  Li and Zheng  Wang and Chen  Ao and Qiang  Fu and Puning  Zhang},
        title={Adaptive fusion localization mechanism towards TDoA and IMU data with LSTM correction Method},
        proceedings={12th EAI International Conference on Mobile Multimedia Communications, Mobimedia 2019, 29th - 30th Jun 2019, Weihai, China},
        publisher={EAI},
        proceedings_a={MOBIMEDIA},
        year={2019},
        month={6},
        keywords={fusion positioning tdoa imu adaptive kalman filter lstm},
        doi={10.4108/eai.29-6-2019.2282992}
    }
    
  • Zhiyuan Liu
    Liang Li
    Zheng Wang
    Chen Ao
    Qiang Fu
    Puning Zhang
    Year: 2019
    Adaptive fusion localization mechanism towards TDoA and IMU data with LSTM correction Method
    MOBIMEDIA
    EAI
    DOI: 10.4108/eai.29-6-2019.2282992
Zhiyuan Liu1, Liang Li2, Zheng Wang2, Chen Ao2, Qiang Fu2, Puning Zhang3,*
  • 1: Maintenance Filiale of State Grid Ningxia Power Co., Yinchuan 750011,Ningxia,China
  • 2: State Grid Key Laboratory of Power Industrial Chip Design and Analysis Technology, Beijing Smart- Chip Microelectronics Technology Co., Ltd. Beijing 100192, China
  • 3: Chongqing University of Posts and Telecommunications, No. 2 Chongwen Road, Nan 'an District, Chongqing, China
*Contact email: zhangpn@cqupt.edu.cn

Abstract

Using Time Difference of Arrival(TDoA) positioning results and the Inertial measurement unit(IMU) for calculating the motion state of information fusion can significantly improve the positioning accuracy, due to the carrier in the process of movement, the state of the system noise and measurement noise are not strictly obey the normal gaussian distribution, which makes the traditional fusion positioning method using Kalman Filtering algorithm less accurate. This paper proposes an adaptive filter fusion localization mechanism with LSTM network correction. Firstly, a data preprocessing method is designed to convert IMU data from the carrier coordinate system to the geographical coordinate system. Then, based on kinematics theory, the state equation and measurement equation of Adaptive Kalman Filter are established and the system state noise is obtained. Furthermore, the model adaptively to update the carrier coordinate, system state noise and measurement noise. Finally, the carrier trajectory coordinates predicted by the coupled LSTM model are used to obtain the final positioning results and complete the carrier trajectory filtering. Experimental results show that the proposed fusion localization mechanism can effectively improve the accuracy of carrier trajectory localization.