基于聚类和TDOA的矿山超宽带定位研究

Jun Dong, Tian Xia
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引用次数: 0

摘要

为了进一步提高地下定位精度,提出了一种基于聚类和TDOA的地下超宽带定位方法。首先采用TDOA方法进行测量,选择Chan算法进行求解,然后将迭代解引入Taylor算法中,建立迭代过程中的数据集合,去除较大误差的迭代值。然后,通过K-means算法对收集到的数据进行聚合。类迭代,过滤数据处理,最终精确定位坐标。很好地解决了泰勒算法初值选择不准确导致的不收敛问题。通过仿真实验,将该算法的性能与Chan算法和Chan Taylor算法进行了比较。该算法的测试结果具有较高的精度和较好的稳定性,虽然定位时间较长,但总体性能较好。升级可以应对复杂的地下环境,实现精确定位。
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Research on Ultra-wideband Location of Mine Based on Clustering and TDOA
To further improve the accuracy of underground positioning, a method of underground Ultra-Wide Band positioning based on clustering and TDOA is proposed. First, the TDOA method is used to measure, Chan algorithm is selected to solve, then the iterative solution is brought into the Taylor algorithm to establish the data collection in the iteration process, and remove the large error iteration values. Then, the data collection is aggregated through the K-means algorithm. Class iteration, filtering data processing and final accurate location coordinates.The non convergence problem caused by inaccurate initial value selection of Taylor algorithm can be well solved. Through simulation experiments, the performance of this algorithm is compared with Chan algorithm and Chan Taylor algorithm. The test results of this algorithm have higher accuracy and better stability, although positioning takes longer, the overall performance is obtained. Upgrading can cope with complex underground environment and achieve accurate positioning.
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