煤矿井下人员定位算法研究有助于在发生矿难后快速开展救援工作，挽救矿难人员生命，最大化程度降低损失。同时高精度定位系统有利于生产过程中人员、设备的调度和管理，对煤矿智能化高效生产具有重大意义。本文具体工作如下：

（1）选用超宽带（Ultra Wide Band，UWB）通信技术和信号到达时间（Time of Arrival，TOA）定位方法对矿井下测距信息进行获取。TOA定位方法易受时钟同步、计时偏移和非视距（Non Line of Sight，NLOS）传播等影响，因此在采用非对称双边双程测距（ADS-TWR）方法有效消除同步时延和计时误差的基础上，针对矿井巷道内NLOS环境对UWB信号传输产生干扰影响测距精度的问题，利用NLOS环境下距离测量值的标准差远大于视距（Line of Sight，LOS）环境下的标准差这一特性，构建误差模型，根据Wylie法原理对NLOS环境下数据进行判别，在卡尔曼滤波（Kalman Filtering，KF）算法构建的测量噪声协方差矩阵中引入NLOS误差转换因子，约束卡尔曼增益及测量残差，补偿NLOS因素造成的正向偏差，提高测距精度。经仿真验证，改进后算法将平均测距误差降低了0.24m，达到了滤除NLOS环境下测距误差的目的。

（2）由于实际定位环境中测距误差不可能完全消除，因此通过研究坐标解析算法以进一步提高定位精度。本文通过抑制NLOS误差后的测距信息构造TOA定位方程，对常用坐标解析算法进行对比分析，发现单一算法在井下系统中定位精度易受基站数目、位置等因素的影响，无法达到理想效果，因此采用麻雀搜索算法（Sparrow Search Algorithm, SSA）为Taylor算法提供初始值，引入佳点集算法对麻雀个体进行排序，同时加入动态学习因子，提高算法收敛速度及全局搜索能力，提出一种ISSA-Taylor定位算法，引入权重系数，通过改进扩展卡尔曼滤波，对定位结果进行误差抑制及数据平滑处理。并通过实验对比证明所提算法对定位精度进行了提高。

（3）针对上述所提算法进行了仿真实验。通过静态实验，本文所提算法在x轴方向和y轴方向的平均误差分别为9.14cm和12.25cm，最大误差分别为14.24cm和19.22cm，平均均方根误差为15.73cm，符合国家矿山安全监察局规定的精确定位要求，且定位精度得到提升；在动态实验中，本文所提算法相较Chan-Taylor算法在误差为20cm的累积分布函数提高了48.76%。实验结果表明本文所提算法可以实现矿井NLOS环境下的高精度定位。

The research of personnel location algorithm in underground coal mine is helpful to carry out the rescue work quickly after the occurrence of mine accident, save the lives of mine accident personnel and reduce the loss to the maximum extent. At the same time, the high-precision positioning system is conducive to the scheduling and management of personnel and equipment in the production process, which is of great significance to the intelligent and efficient production of coal mine. The specific work of this paper is as follows:

(1) Ultra Wide Band (UWB) communication technology and Time of Arrival (TOA) positioning method were used to obtain the ranging information of underground mine. TOA positioning method is easily affected by clock synchronization, timing offset and Non Line of Sight (NLOS) propagation, etc. In this paper, on the basis of effectively eliminating synchronization delay and timing error by using asymmetric bilateral distance measurement (ADS-TWR) method, Aiming at the problem that the NLOS environment in mine roadway interferes with the signal transmission of UWB and affects the ranging accuracy, the error model is constructed based on the characteristic that the standard deviation of distance measurement value in NLOS environment is much larger than that in Line of Sight (LOS) environment, and Wylie method is used to discriminate the data in NLOS environment. The NLOS error conversion factor was introduced into the measurement noise covariance matrix constructed by Kalman Filtering (KF) algorithm to restrict the Kalman gain and measurement residual, and to compensate the forward deviation caused by NLOS factors to improve the ranging accuracy. The simulation results show that the proposed algorithm reduces the average ranging error by 0.24m and achieves the purpose of filtering the ranging error in NLOS environment.

(2) Since the ranging error cannot be completely eliminated in the actual positioning environment, coordinate analysis algorithm is studied to further improve the positioning accuracy. In this paper, the TOA positioning equation is constructed by the distance measurement information after the suppression of NLOS error, and the common coordinate analysis algorithms are compared and analyzed. It is found that the positioning accuracy of a single algorithm is easily affected by the number and location of the base station and other factors in the downhole system, and can not achieve the ideal effect. In this paper, Sparrow Search Algorithm (SSA) is used to provide initial value for Taylor algorithm, and a good point set algorithm is introduced to sort individual sparrows. At the same time, dynamic learning factors are added to improve the convergence speed and global search ability of the algorithm. An ISSA-Taylor positioning algorithm is proposed. By introducing the weight coefficient and improving the extended Kalman filter, the error suppression and data smoothing are carried out. The experimental results show that the proposed algorithm improves the positioning accuracy.

(3) A large number of simulation experiments have been carried out for the proposed algorithm. Through static experiments, the average error of the proposed algorithm in x axis direction and y axis direction is 9.14cm and 12.25cm, the maximum error is 14.24cm and 19.22cm, and the average root mean square error is 15.73cm, which meets the precise positioning requirements stipulated by the National Administration of Mine Safety, and the positioning accuracy has been improved. In the dynamic experiment, compared with the Chan-Taylor algorithm, the cumulative distribution function of 20cm is improved by 48.76%. Experimental results show that the proposed algorithm can achieve high precision location in mine NLOS environment.

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