摘要
针对超宽带(UWB)在噪声变化较大、统计特性未知的环境中无法精确定位的问题,提出了基于改进的无迹卡尔曼滤波(UKF)算法的多传感器融合定位方法。对UWB、IMU和里程计编码器信息进行集中式融合,引入自适应因子实时更新量测噪声的协方差矩阵以更新量测噪声,加入渐消因子抑制滤波发散,实现对传统UKF算法的改进。基于两轮差速机器人和UWB硬件平台,分别在UWB视距(LOS)和非视距(NLOS)场景下,对所提的融合定位方法进行了仿真分析和实验验证,并与UWB、IMU等单一传感器定位以及扩展卡尔曼滤波(EKF)、UKF算法进行比较。结果表明,所提的融合定位方法在LOS和NLOS场景下都可保持较高的定位精度,且在NLOS场景下改进的UKF算法比EKF和UKF算法的定位精度分别提高了约22.8%和13.1%。
A multi-sensor fusion positioning method based on the improved unscented Kalman filter(UKF)algorithm is proposed to address the problem that ultra wide band(UWB)cannot be precisely located in the environment with unknown statistical properties with large noise variations.The information of UWB,IMU and odometer encoder is fused centrally.An adaptive factor is introduced to update the measurement noise covariance matrix in real time to update the observation noise,and the fading factor is added to suppress the filtering divergence to achieve an improvement for the traditional UKF algorithm.Based on the two-wheel differential robot and UWB hardware platform,the proposed fusion positioning method is evaluated by simulation and field experiment in UWB line-of-sight(LOS)and non-line-of-sight(NLOS)scenarios respectively,and compared with the single-sensor positioning including UWB,IMU,and fusion algorithms based on the extended Kalman filter(EKF)and UKF.The results show that the proposed fusion positioning method can maintain high accuracy in both LOS and NLOS scenarios,and the improved UKF algorithm improves the positioning accuracy by about 22.8%and 13.1%over EKF and UKF algorithms respectively in NLOS scenarios.
作者
杨秀建
皇甫尚昆
颜绍祥
YANG Xiujian;HUANGFU Shangkun;YAN Shaoxiang(Faculty of Transportation Engineering,Kunming University of Science and Technology,Kunming 650500,China)
出处
《中国惯性技术学报》
EI
CSCD
北大核心
2023年第5期462-471,共10页
Journal of Chinese Inertial Technology
基金
国家自然科学基金(52162046)。
关键词
移动机器人
超宽带
定位
多传感器融合
无迹卡尔曼滤波
mobile robot
ultra wide band
positioning
multi-sensor fusion
unscented Kalman filter
