随着定位技术的快速发展，基于超宽带(UWB) 定位技术在井下定位领域应用具有十分重要的研究意义与实际价值。井下环境中存在诸多干扰以及障碍物，使得UWB信号传输过程中产生多径干扰以及非视距（NLOS）误差，从而影响定位精度。因此本文针对井下NLOS环境下基于UWB的移动目标定位算法展开研究。

       针对井下环境，UWB定位使用到达时间差（TDOA）算法存在的波束指向性低以及多径误差等问题，基于经典TDOA算法进行改进。改进的方法首先计算信号的到达时间差，对多条路径进行分离和重构，以解决不同参考节点对测距结果的影响，在无需参考节点的情况下就能实现异步时钟下的测距；引入动态滤波修正异常值，运用双边滤波算法对测距值进行优化。实验结果表明改进后的TDOA算法能够有效减小测距误差，满足井下定位对实时性和可靠性要求。

      为解决经典TDOA井下测距算法在NLOS环境中测距精度低、误差较大的问题，对卡尔曼滤波算法进行优化。优化方法首先引入经典卡尔曼滤波算法对测距值进行视距重构，减小非视距对测距精度的影响。对初次估计值进行NLOS误差判别，依据判别结果进行视距重构；同时将仅受视距（LOS）误差影响的历史值用来更新卡尔曼滤波的协方差阵；基于卡尔曼滤波算法更新协方差阵，通过对接收信号进行处理并迭代，达到米级的移动目标定位精度。在NLOS环境下，优化后算法的均方根误差（RMSE）值为0.6196m，相较于其他相关算法，能够更有效地提高测距精度，减小测距误差。

       为解决经典Chan算法在井下复杂环境中存在NLOS误差导致定位精度低的问题，以及初值不准确使得Taylor级数展开法无法收敛的问题进行改进。改进的算法首先利用Chan算法得到TDOA差值并进行NLOS误差鉴别和修正，然后再次估计标签节点的位置坐标；利用这个修正后的位置坐标作为Taylor算法的初始值进行迭代；最后，通过设置合理的加权系数来修正最终的估计值。这种算法能够在井下复杂环境中有效提高定位精度，实验结果表明，井下NLOS环境下，优化后的定位算法相较于对比算法表现出更好的定位性能，RMSE值为0.9365m。

With the rapid development of positioning technology, the application of ultra wideband (UWB) positioning technology in the field of coal mine positioning has significant research significance and practical value. There are many interferences and obstacles in the coal mine environment, which cause multipath interference and non-line of sight (NLOS) errors during UWB signal transmission, thereby affecting positioning accuracy. Therefore, this article focuses on the research of UWB based mobile target localization algorithm in the coal mine NLOS environment.

In view of the coal mine environment, Time difference of arrival (TDOA) algorithm for UWB positioning has problems such as low beam directivity and multipath error, which are improved based on the classic TDOA algorithm. The improved method first calculates the Pseudo-range multilateration of the signal, separates and reconstructs multiple paths to solve the impact of different reference nodes on the ranging results, and can achieve the ranging under asynchronous clock without reference nodes; Dynamic filtering is introduced to correct Outlier, and bilateral filtering algorithm is used to optimize ranging values. The experimental results show that the improved TDOA algorithm can effectively reduce ranging errors and meet the real-time and reliability requirements for coal mine positioning.

The Kalman filter algorithm is optimized to solve the problem of low precision and large error of the classical TDOA downhole ranging algorithm in NLOS environment. The optimization method first introduces the traditional Kalman filter algorithm to reconstruct the line of sight (LOS) of the ranging value, so as to reduce the impact of non-line of sight (NLOS) on the ranging accuracy. Perform NLOS error discrimination on the initial estimated value, and reconstruct the line of sight based on the discrimination results; At the same time, the historical value affected only by LOS error is used to update the covariance matrix of Kalman filter; The covariance matrix is updated based on the Kalman filter algorithm, and the received signal is processed and iterated to achieve the meter level mobile target positioning accuracy. In the NLOS environment, the Root-mean-square-error (RMSE) value of the optimized algorithm is 0.6196m. Compared with other related algorithms, it can effectively improve the ranging accuracy and reduce the ranging error.

In order to solve the problem of low positioning accuracy caused by NLOS error in the traditional Chan algorithm in the complex coal mine environment, and the problem of Taylor Series expansion method unable to converge due to the inaccuracy of the initial value, improvements were made. The improved algorithm first uses the Chan algorithm to obtain the TDOA difference and perform NLOS error identification and correction, and then estimates the position coordinates of the label nodes again; Use this corrected position coordinate as the initial value of the Taylor algorithm for iteration; Finally, correct the final estimated value by setting reasonable weighting coefficients. This algorithm can effectively improve positioning accuracy in complex coal mine environments. Experimental results show that in the NLOS environment, the optimized positioning algorithm exhibits better positioning performance compared to the comparative algorithm, with an RMSE value of 0.9365m..

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