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Positioning and tracking with ODE-LSTM algorithm for emerging smart rail systems

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Abstract

In this paper, we propose a NLOS positioning and tracking method in order to be applied in the emerging smart rail systems. By analyzing MIMO scatter channels, the geometric relationship for positioning between each UE, BS and scattering points can be modeled that includes information of AOA, AOD, TOA. The accuracy of positioning can be improved by forming an optimization problem with bias in time and orientation. Further, in order to track the mobile UE, an ODE-LSTM algorithm is proposed, which is combined by ODE derivation solver and LSTM network. It puts the positioning information into an ODE-LSTM network to achieve continuous tracking in arbitrary time instance. Simulation works validate that the proposed ODE-LSTM method shows better performance in nonlinear tracking than traditional Kalman filter or enhanced Kalman filter, demonstrating a performance improvement of at least 10%.

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Author notes

  1. Feng Tian and Ziling Shao have contributed equally to this work.

Authors and Affiliations

  1. Key Laboratory of Ministry of Education, Nanjing University of Posts and Telecommunications, Xinmofan Street, Nanjing, 210003, Jiangsu, China

    Xiao Zhao, Feng Tian & Ziling Shao

Authors
  1. Xiao Zhao
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  2. Feng Tian
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  3. Ziling Shao
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Contributions

X.Z.: Conceptualization and Methodology; F.T.: Writing - Review & Editing and Supervision; Z.S.: Data Curation

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Correspondence to Feng Tian.

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Zhao, X., Tian, F. & Shao, Z. Positioning and tracking with ODE-LSTM algorithm for emerging smart rail systems. J Supercomput 80, 21975–21995 (2024). https://doi.org/10.1007/s11227-024-06296-2

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