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The inertial navigation systems exhibit excellent short-term positioning accuracy, yet they are susceptible to cumulative errors over time. WiFi fingerprint localization avoids cumulative errors, but it is prone to mismatching issues. Therefore, a commonly used technique is the integration of an inertial navigation system and WiFi fingerprint matching.The particle filter employs dead reckoning (DR) for the state transfer equation, while utilizing the disparity between inertial navigation and WiFi fingerprint matching as the observation equation. Floor map information is introduced to detect whether particles cross the wall and if so, the weight is set to zero. For the particles that do not cross the wall, considering the distance between the current particles and the historical particles, an adaptive particle filter is proposed. The adaptive factor increases the weight of highly trusted particles and reduces the weight of untrusted particles. Another innovation is the introduction of a multidimensional Euclidean distance algorithm to reduce inconsistencies in WiFi fingerprint matching. The experimental results show that the proposed algorithm achieves high positioning accuracy.

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An Indoor Navigation Algorithm Using Multi-dimensional Euclidean Distance and the Adaptive Particle Filter

  • 会议论文

  • 发表日期：

    2025年

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  • Yunbing Hu,
  • Ao Peng,
  • Shenghong Li

摘要

The inertial navigation systems exhibit excellent short-term positioning accuracy, yet they are susceptible to cumulative errors over time. WiFi fingerprint localization avoids cumulative errors, but it is prone to mismatching issues. Therefore, a commonly used technique is the integration of an inertial navigation system and WiFi fingerprint matching.The particle filter employs dead reckoning (DR) for the state transfer equation, while utilizing the disparity between inertial navigation and WiFi fingerprint matching as the observation equation. Floor map information is introduced to detect whether particles cross the wall and if so, the weight is set to zero. For the particles that do not cross the wall, considering the distance between the current particles and the historical particles, an adaptive particle filter is proposed. The adaptive factor increases the weight of highly trusted particles and reduces the weight of untrusted particles. Another innovation is the introduction of a multidimensional Euclidean distance algorithm to reduce inconsistencies in WiFi fingerprint matching. The experimental results show that the proposed algorithm achieves high positioning accuracy.