基于UWB的地下定位算法和拓扑优化

An underground localization algorithm and topology optimization based on ultra-wideband

地下定位面对环境恶劣、干扰、多径等影响,常规算法难以获得高精度的定位结果,同时井下环境多为狭长的巷道,不利于布置定位所需的锚节点,而井下锚节点的布置通常对定位结果有较大影响,因而使用普通的定位方法不足以满足智能采矿所需的高精度定位需求.本文对传统的三边定位算法进行分析,总结了传统三边定位结果产生误差的原因,并提出了改进的算法,通过仿真实验验证了改进算法的有效性.同时通过理论分析误差带,使用最大绝对定位误差用于仿真分析拓扑结构对定位结果精度的影响,提出了对拓扑结构的优化原则,能够根据环境特点以实现定位区域内平均最大绝对定位误差最小为原则得出最优拓扑结构.文中设置了仿真实验和实地实验对改进的算法和拓扑结构优化方法进行了验证,实验结果中,改进的算法能够在相同拓扑结构下减小15%-43%的误差,而在相同算法下优化的拓扑结构能够减小17%-65%,二者结合能够减小误差达74%.结果表明,在相同的定位条件下,改进的定位算法能够明显提高定位结果的精度,同时定位结果与拓扑结构之间也有着密切的联系,根据实际环境灵活布置拓扑结构能够使定位结果的精度进一步提高,将改进的算法与拓扑结构优化方法结合可以实现更高的定位精度.

Development of context-aware technologies and rapid advancement in communication are essential parts of most industries,such as underground mining,pervasive medical care,smart space,and wireless sensor network surveillance. On occasions that require positioning services,location techniques offer convenience and may even save lives. In underground mining,not only do the miners work in a hostile environment but the environment itself threatens their lives. Thus,determining the precise location of people and objects in underground environments is essential. However,GPS cannot be used for practical application underground and underground localization seems to be the most feasible way to provide positional information to mining vehicles. The localization of underground vehicles has been a critical obstacle in the development of intelligent mining vehicles. Faced with the bad environment,interference,and multipaths among other effects,it is difficult to obtain high-precision positioning results using conventional algorithms. In addition,the underground environment is mostly made up of long narrow tunnels,which is not conducive to an arrangement of anchor nodes,and the layout of underground anchor nodes usually has great influence on the positioning results. Therefore,ordinary positioning methods do not meet the high-precision positioning requirements of intelligent mining. In this paper,traditional trilateration was analyze,the reasons for error was summarized,and an improved algorithm was proposed. Simulation results showed the effectiveness ofthe improved algorithm. In addition,the principle of topology optimization was proposed by theoretically analyzing the error band and using the maximum absolute positioning error to simulate the influence of topology on positioning accuracy. According to the characteristics of the environment,minimizing the average maximum absolute positioning error was the principle of topology optimization. Here,simulation and field experiments were carried out to verify the improved algorithm and topology optimization method. The experimental results show that the improved algorithm can reduce the error by 15%-43% under the same topology,the optimized topology can reduce the error by 17%-65%,and a combination of the two can reduce the error by 74%. The results show that under the same localization conditions,the proposed algorithm can significantly improve the accuracy of the localization results. In addition,there is a close relationship between localization result and topology structure. Based on the actual environment,choosing a flexible topology layout can further improve positioning accuracy,and combining the improved algorithm with the topology optimization method can achieve a higher positioning accuracy.