摘要
为提高井下超宽带定位的精度,提出一种基于改进卡尔曼滤波(improved Kalman filter, IKF)与萤火虫优化粒子滤波(FA-PF)的目标跟踪定位算法。首先利用IKF对标签与基站之间的测距值进行降噪处理;然后利用萤火虫算法改进粒子滤波的重采样过程,使算法在迭代过程中保持粒子的多样性,提高滤波的精度;最后使用FA-PF算法估计目标位置。仿真结果表明,相比传统卡尔曼滤波,IKF抑制非视距(non line of sight, NLOS)噪声的能力有所提高;在测量值相同的情况下,FA-PF算法具有更高的定位精度,定位误差相比粒子滤波算法和卡尔曼滤波算法分别降低了43.98%和32.50%。
In order to improve the accuracy of UWB positioning in mines, a target tracking and localization algorithm based on improved Kalman filter(IKF) and firefly optimized particle filter(FA-PF) is proposed. Firstly, IKF is used to denoise the ranging value between the tag and the base station;then the firefly algorithm is used to improve the resampling process of particle filtering, so that the algorithm can maintain the diversity of particles in the iterative process and improve the filtering accuracy;finally, the FA-PF algorithm is used to estimate the target position. The simulation results show that, compared with the traditional Kalman filter, the IKF′s ability to suppress non-line of sight(NLOS) noise has been improved. In the case of the same measurement value, the FA-PF algorithm has higher positioning accuracy, and the positioning error is reduced by 43.98% and 32.50% compared with the particle filter algorithm and the Kalman filter algorithm, respectively.
作者
周伯宇
孙洁
史元良
Zhou Boyu;Sun Jie;Shi Yuanliang(School of Electrical Engineering,North China University of Technology,Tangshan 063200,China;China Aerospace Construction Group Co.,Ltd.,Beijing 10o071,China)
出处
《国外电子测量技术》
北大核心
2022年第8期39-45,共7页
Foreign Electronic Measurement Technology
基金
河北省自然科学基金(E2019209492)项目资助。
关键词
井下定位
超宽带
改进卡尔曼滤波
萤火虫算法
粒子滤波
underground location
ultra-wideband
improved Kalman filter
firefly algorithm
particle filter
