摘要
[目的]为提高车辆组合定位系统的定位精度与系统内可靠性,提出一种基于新息探测准则的自适应无迹卡尔曼滤波联邦数据融合方法(innovation detection adaptive unscented Kalman filter-federated Kalman filter,IDAUKFFKF)。[方法]当传感器因故障或环境干扰产生含粗差及其他异常的观测数据时,该算法通过新息探测技术对观测信息进行检测,识别并剔除异常量测数据;同时,采用噪声估计器对局部滤波器子系统的量测噪声统计特性进行在线调整,以此约束局部估计误差对全局滤波结果的影响。[结果]所提多传感器数据融合方法能精准剔除异常观测数据并优化局部估计效果:相较于传统联邦卡尔曼滤波(federated Kalman filter,FKF)与无迹卡尔曼滤波(unscented Kalman filter,UKF)算法,该算法使系统位置均方根误差分别降低约44.15%和34.70%,显著提升了车辆组合定位系统的定位精度与抗干扰性能。[结论]所提多传感器数据融合方法可为复杂环境下车辆高精度定位提供可行的技术方案。
[Proposes]To enhance the positioning accuracy and system internal reliability of the vehicle positioning system,this paper proposed an adaptive unscented Kalman filter data fusion method based on the innovation detection criterion(innovation detection adaptive unscented Kalman filter-federated Kalman filter,IDAUKF-FKF).[Methods]When the sensors produce abnormal measurement data due to faults or interference,containing gross errors or other disturbances,this algorithm can detect the observation information based on the innovation detection criterion,identify and eliminate the abnormal measurement data.Additionally,a noise estimator was employed to adjust the statistical characteristics of the measurement noise in the local filter subsystems online,constraining the impact of local estimation errors on the global filtering.[Findings]Simulation results demonstrate that the proposed multi-sensor data fusion algorithm can accurately eliminate abnormal observations and optimize local estimates.Compared to federated Kalman filtering(FKF)and unscented Kalman filter(UKF),the RMSE for position errors is reduced by approximately 44.15%and 34.70%,respectively,significantly improving the positioning accuracy and anti-interference performance of the vehicle positioning system.[Conclusions]The proposed method provides a feasible technical solution for high-precision vehicle positioning in complex environments.
作者
孟阳
冯京晓
王进达
高雅
MENG Yang;FENG Jingxiao;WANG Jinda;GAO Ya(College of Physics&Electronic Information,Luoyang Normal University,Luoyang 471934,China;Engineering Research Center of Advanced Robot Control and Intelligence Information Processing,Luoyang 471934,China)
出处
《交通科学与工程》
2025年第5期58-67,共10页
Journal of Transport Science and Engineering
基金
国家自然科学基金项目(62301241)
河南省高等学校重点科研项目(23A580003、24A120012、23A470013)。
