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<p>Due to multiple occlusions and strong interference in an indoor environment, the traditional single signal source location method is difficult to meet the requirements of high precision and high robustness. Therefore, this paper proposes a multi-source fusion location system based on an adaptive vector particle filter, which combines fingerprint location, pedestrian dead reckoning and map information. The received signal intensity is optimized by offline fingerprint calibration and Kalman filter. The adaptive vector particle filter adopts multi-direction sampling and weight adjustment, which effectively improves the diversity of particles and reduces errors. Compared with the single-source method and other multi-source systems, the positioning accuracy and trajectory fitting degree of the proposed system were significantly improved. The positioning error probability was 97%, the average error was 0.67&#xa0;m, and the positioning accuracy reached 90.1%. In summary, the proposed multi-source fusion system provides an effective solution for indoor high-precision and reliable positioning.</p>

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A Multi-Source Fusion Positioning System based on MDAW-PF Algorithm and PDR

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年2月18日

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  • Wennan Wu,
  • Yigang Xu,
  • Zhimin Li,
  • Jizhou Lai

摘要

Due to multiple occlusions and strong interference in an indoor environment, the traditional single signal source location method is difficult to meet the requirements of high precision and high robustness. Therefore, this paper proposes a multi-source fusion location system based on an adaptive vector particle filter, which combines fingerprint location, pedestrian dead reckoning and map information. The received signal intensity is optimized by offline fingerprint calibration and Kalman filter. The adaptive vector particle filter adopts multi-direction sampling and weight adjustment, which effectively improves the diversity of particles and reduces errors. Compared with the single-source method and other multi-source systems, the positioning accuracy and trajectory fitting degree of the proposed system were significantly improved. The positioning error probability was 97%, the average error was 0.67 m, and the positioning accuracy reached 90.1%. In summary, the proposed multi-source fusion system provides an effective solution for indoor high-precision and reliable positioning.