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Fingerprinting Localization Method Based on TOA and Particle Filtering for Mines

机译：基于TOA和粒子滤波的矿山指纹定位方法

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摘要

Accurate target localization technology plays a very important role in ensuring mine safety production and higher production efficiency. The localization accuracy of a mine localization system is influenced by many factors. The most significant factor is the non-line of sight (NLOS) propagation error of the localization signal between the access point (AP) and the target node ( Tag). In order to improve positioning accuracy, the NLOS errormust be suppressed by an optimization algorithm. However, the traditional optimization algorithms are complex and exhibit poor optimization performance. To solve this problem, this paper proposes a new method for mine time of arrival (TOA) localization based on the idea of comprehensive optimization. The proposed method utilizes particle filtering to reduce the TOA data error, and the positioning results are further optimized with fingerprinting based on the Manhattan distance. This proposed method combines the advantages of particle filtering and fingerprinting localization. It reduces algorithm complexity and has better error suppression performance. The experimental results demonstrate that, as compared to the symmetric double-sided two-way ranging (SDS-TWR) method or received signal strength indication (RSSI) based fingerprinting method, the proposed method has a significantly improved localization performance, and the environment adaptability is enhanced.
机译：准确的目标定位技术在确保矿山安全生产和提高生产效率方面发挥着非常重要的作用。矿井定位系统的定位精度受许多因素影响。最重要的因素是在接入点（AP）和目标节点（Tag）之间的定位信号的非视线（NLOS）传播误差。为了提高定位精度，必须通过优化算法来抑制NLOS误差。但是，传统的优化算法比较复杂，优化性能较差。为了解决这个问题，本文基于综合优化的思想，提出了一种新的矿井到达时间定位方法。所提出的方法利用粒子滤波来减少TOA数据误差，并且基于曼哈顿距离的指纹识别进一步优化了定位结果。该方法结合了粒子滤波和指纹定位的优势。它降低了算法复杂度，并具有更好的错误抑制性能。实验结果表明，与对称双向双向测距（SDS-TWR）方法或基于接收信号强度指示（RSSI）的指纹方法相比，该方法具有明显的定位性能和环境适应性。被增强。

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  • 来源
    《Mathematical Problems in Engineering》 |2017年第9期|3215978.1-3215978.10|共10页
  • 作者

    Song Boming; Zhang Shen; Long Jia; Hu Qingsong;

  • 作者单位

    China Univ Min & Technol, Internet Things Percept Mine Res Ctr, Xuzhou 221008, Peoples R China;

    China Univ Min & Technol, Internet Things Percept Mine Res Ctr, Xuzhou 221008, Peoples R China;

    China Univ Min & Technol, Internet Things Percept Mine Res Ctr, Xuzhou 221008, Peoples R China;

    China Univ Min & Technol, Internet Things Percept Mine Res Ctr, Xuzhou 221008, Peoples R China;

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