煤炭在我国一次能源生产和消费结构中占有主要地位。煤矿人员定位系统作为“安全避险六大系统”之一，在煤矿生产管理和事故应急救援中发挥了重要作用，但目前我国主流煤矿人员定位方法误差较大。因此深入研究煤矿井下人员定位误差的成因及抑制方法，对提高矿井人员定位准确性、保障煤矿安全生产具有重要意义。

本文以煤矿井下人员定位方法为研究对象，在分析对比目前主流无线定位方法后，以时间到达法（TOA）的测距方法为基础，首先对该方法下的测距误差进行深入分析，在采用双边双路通信方式下，得出非高斯分布的非视距误差（NLOS）是人员测距误差的主要来源；为抑制非视距误差对测距结果的影响，提出以高斯和滤波理论为基础的鲁棒卡尔曼和滤波（RKSF）测距优化方法。其次分析巷道特殊几何尺寸对传统人员定位方法的影响，在长宽比趋于无穷的巷道环境中，定位误差主要由巷道径向方向承担；为适应巷道定位误差的这种特点并增强定位算法对异常观测的鲁棒性，提出以假设检验为基础的鲁棒无迹卡尔曼滤波（RUKF）定位优化方法。最后构建基于RKSF-RUKF模型的煤矿井下人员定位方法。为验证所提方法的有效性，设计并实现基于机器人操作系统（ROS）和Gazebo平台的仿真实验，对煤矿井下定位中常见的三种场景进行对比分析。

经仿真实验，在视距环境下，本文方法与传统方法差距不大；在非视距环境下，本文方法具有较好的误差抑制能力，验证了本文方法的有效性，为提高矿井人员定位精度、保障煤矿安全高效生产、辅助矿井应急救援，提供了一定借鉴意义。

Coal occupies a major position in Chinese primary energy production and consumption structure. As one of the "six safety avoidance systems", the coal mine personnel positioning system has played an important role in coal mine production management and accident emergency rescue. However, the current mainstream coal mine personnel positioning methods in my country have relatively large errors. Therefore, it is of great significance that in-depth research on the causes and suppression methods of personnel positioning errors in coal mines for improving the accuracy of mine personnel positioning and ensuring coal mine safety production.

The method of personnel positioning in coal mines is treated as the research object in this paper. After analyzing and comparing the current mainstream wireless positioning methods, based on the time-of-arrival (TOA) ranging method, firstly conducting an in-depth analysis of the ranging error under this method, it is concluded that the non-Gaussian distributed non-line-of-sight error (NLOS) is the main source of the personnel ranging error under the symmetric double-sided two way ranging communication mode. In order to suppress the influence of the non-line-of-sight error on the ranging result, it is proposed to base on the Gaussian sum filtering theory robust Kalman sum filtering (RKSF) ranging optimization method. Secondly, the influence of the special geometric dimensions of the roadway is analyzed on the traditional personnel positioning method. In the roadway environment where the aspect ratio tends to be infinite, the positioning error is mainly borne by the radial direction of the roadway. In order to adapt to this characteristic of roadway positioning error and enhance the robustness of the positioning algorithm to abnormal observations, a robust unscented Kalman filter (RUKF) positioning optimization method based on hypothesis testing is proposed. Finally, a method for locating people underground in coal mines based on the RKSF-RUKF model is constructed. For the purpose of verifying the effectiveness of the proposed method, a simulation experiment based on Robot Operating System (ROS) and Gazebo platform was designed and implemented, and the three most common positioning scenarios in underground coal mine positioning were compared and analyzed.

Through simulation experiments, in the line-of-sight environment, the method in this paper is not far from the traditional method; in the non-line-of-sight environment, the method in this paper has a good error suppression ability, which verifies the effectiveness of the method in this paper and improves the positioning accuracy of mine personnel, guaranteeing the safe and efficient production of coal mines, and assisting mine emergency rescue, provide a certain reference significance.

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