摘要
针对煤矿井下定位复杂度高,定位精度要求高等特点。为提升煤矿井下精确定位的精度与效率,保证人员安全等需要,提出了基于改进TDOA的超宽带定位算法。根据井下巷道条件不同,采用不同训练与滤波的方法,最大程度保证了井下定位的精度与效率。实验表明,联合算法在视距与非视距情况下,定位均值精度均能够保持在10 cm以下,且与基础TDOA算法相比,在视距与非视距条件下,均值精度分别提高了20与35 cm;而在视距情况下,运用训练完成的神经网络进行高速定位,提升了定位效率。因此,联合算法能够实现在不同环境下定位精度与定位效率的兼顾,可满足井下高精度定位的要求,适用于矿山井下人员定位。
Aiming at the characteristics of high complexity and high accuracy of underground coal mine positioning. For improving the accuracy and efficiency of coal mine precise positioning and ensure the safety of personnel, an ultra wideband positioning algorithm based on improved TDOA is proposed. According to the different conditions of underground roadway, different training and filtering methods are adopted to ensure the accuracy and low complexity of underground positioning. The experimental results show that the average positioning accuracy of the combined algorithm can be maintained below 10 cm in both LOS and NLOS, and compared with the basic TDOA algorithm, the average positioning accuracy is improved by 20 and 35 cm in LOS and NLOS, respectively;while in LOS, the trained neural network is used for high-speed positioning to improve the positioning efficiency. Therefore, the combined algorithm can achieve the balance of positioning accuracy and efficiency in different environments, meet the requirements of high-precision positioning underground, and is suitable for underground personnel positioning.
作者
陈浩
李起伟
王子龙
Chen Hao;Li Qiwei;Wang Zilong(Coal Science and Technology Research Institute Co.,Ltd.,Beijing 100013,China;State Key Laboratory for Efficient Mining and Clean Utilization of Coal Resources,Beijing 100013,China;Beijing Coal Mine Safety Engineering Technology Research Center,Beijing 100013,China)
出处
《电子测量技术》
北大核心
2021年第6期96-102,共7页
Electronic Measurement Technology
基金
天地科技股份有限公司科技创新创业资金专项(2020-TD-MS001)
煤科院基础基金项目(2019CX-Ⅱ-15)资助。
关键词
精确定位
改进TDOA算法
BP神经网络
粒子滤波
视距
非视距
precise location
improved TDOA algorithm
BP neural network
particle filter
sight distance
non-sight distance
