基于UWB与指纹定位的矿井移动目标TOA定位算法  

TOA Localization Algorithm of Underground Mine Moving Target Based on UWB and Fingerprint Localization

作　　者：王智勇 张宏伟[1,2] 卜旭辉 WANG Zhiyong;ZHANG Hongwei;BU Xuhui(School of Electrical Engineering and Automation,Henan Polytechnic University,Jiaozuo,Henan 454003,China;Henan Key Laboratory of Intelligent Detection and Control of Coal Mine Equipment,Jiaozuo,Henan 454003,China)

机构地区：[1]河南理工大学电气工程与自动化学院,河南焦作市454003 [2]河南省煤矿装备智能检测与控制重点实验室,河南焦作市454003

出　　处：《矿业研究与开发》2024年第3期192-200,共9页Mining Research and Development

基　　金：国家自然科学基金项目(U1804147);河南省高校科技创新团队项目(20IRTSTHN019).

摘　　要：针对煤矿井下人员、设备等目标的定位易受非视距传播时延影响,导致定位精度低、实时性不高等问题,提出了一种基于超宽带(UWB)与指纹定位的矿井移动目标到达时间(TOA)定位方法。首先,采用双程双向测距(DS-TWR)方式测量定位基站与待测目标之间的距离,构建Chan算法估算待测目标的坐标;其次,利用Taylor公式对Chan算法的定位结果进行迭代更新,抑制矿井巷道中非视距(NLOS)延时误差;最后,依次采集特定点距离指纹构建指纹库,引入改进的算术优化算法优化最小二乘支持向量机(AOA-LSSVM)模型估计待测目标位置的横、纵坐标误差,结合Chan-Taylor算法定位结果进行误差补偿,得到待测目标的最优位置估计。试验结果表明:所提出的算法在视距(LOS)环境下的静态试验和动态试验中定位精度相较于Chan-Taylor算法分别提升了18.63%、63.79%;在NLOS环境下的静态试验和动态试验中定位精度相较于Chan-Taylor算法分别提升了82.40%、56.78%,可满足目标在矿井下高精度的定位要求。Aiming at the problem that the localization of targets such as personnel and equipment in underground mine is easily affected by non-line-of-sight propagation delay,resulting in low localization accuracy and poor real-time performance,a time of arrival(TOA)localization method for underground mine moving targets based on ultra-wide band(UWB)and fingerprint localization was proposed.Firstly,the distance between the localization base station and the target to be measured was measured by double-sided two-way ranging(DS-TWR),and the Chan algorithm was constructed to estimate the coordinates of the target to be measured.Secondly,the Taylor formula was used to iteratively update the localization results of the Chan algorithm to suppress the non-line-of-sight(NLOS)delay error in the mine roadway.Finally,the fingerprint database was constructed by collecting the distance fingerprints of specific points in turn.The horizontal and vertical coordinate errors of the target position to be measured were estimated by the improved arithmetic optimization algorithm to optimize the least squares support vector machine(AOA-LSSVM)model.Combined with the localization results of Chan-Taylor algorithm,the error compensation was carried out to obtain the optimal position estimation of the target to be measured.The experimental results show that the localization accuracy of the proposed algorithm is improved by 18.63%and 63.79%respectively compared with the Chan-Taylor algorithm in the static and dynamic experiments in the line-of-sight(LOS)environment.The localization accuracy of the algorithm is improved by 82.40%and 56.78%respectively compared with the Chan-Taylor algorithm in the static and dynamic experiments in NLOS environment,which can meet the high-precision localization requirements of the target in underground mine.

关 键 词：矿井移动目标 超宽带 指纹定位 时间到达定位算法 Chan-Taylor算法 

分 类 号：TD65[矿业工程—矿山机电]