面向矿井动目标的PSO-SVR模型与UWB Chan优化距离指纹融合定位方法

针对目前井下人员、车辆、设备等移动目标位置精确管理存在的不足,本文对面向矿井动目标的定位算法与指纹定位模 型进行研究。 设计出一种基于改进粒子群优化 SVR 模型与 Chan 优化距离指纹匹配融合定位方法。 首先,构建一种基于 STM32 ARM 主控制器和 DWM1000 的超宽带(UWB)核心节点模型,通过双边双向测距和飞行时间法(TOF)对传输距离数据进 行计算。 在此基础上,通过依次在特定点采集距离指纹,基于改进的 PSO-SVR 模型进行移动目标路线拟合,预测目标的移动路 径。 再将其与 Chan 指纹进行结合,拓展出优化距离指纹融合定位方法。 实验结果表明,本文提出的指纹优化匹配融合定位方 法能够较好地预测出移动路径,最大误差不超过 20 cm,平均误差不超过 1 cm。 本文研究对矿井智能化建设及安全生产具有重 要意义。

Fusion location method of PSO-SVR model and UWB Chan optimal fingerprint matching for mine moving target

Aiming at improving the deficiency of positioning accuracy of moving targets such as underground personnel, vehicles and equipment, this paper studies the location algorithm and fingerprint location model of mine moving target and a fusion location method based on SVR model optimized by improved particle swarm optimization and Chan distance fingerprint is proposed. Firstly, an ultra wideband (UWB) core node model based on STM32 ARM main controller and DWM1000 is designed, and the transmission distance data are analyzed through bilateral bidirectional ranging and time of flight ( TOF). On this basis, the moving path of the target is predicted by successively collecting distance fingerprints at specific points and the moving target route fitting within the improved PSOSVR model. Then it is combined with the Chan algorithm fingerprint, and expand the optimized distance fingerprint fusion location method. The experimental results show that the optimized distance fingerprint fusion location method can correctly predict the moving path, with the maximum error of no more than 20 cm and the average error of no more than 1 cm. The study is of great significance to mine intelligent construction and safety production.