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车辆操纵稳定性状态估计算法比较研究 被引量：3

Comparative Study of Some Estimation Algorithms for Vehicle Stability State
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摘要 建立了基于运动学的车辆3自由度状态估计模型,分别将扩展卡尔曼滤波(EKF)、无迹卡尔曼滤波(UKF)和粒子滤波(PF)应用到车辆状态估计中,通过仿真试验比较了3种算法的估计效果。结果表明,车辆工作在线性稳定区域时,EKF算法效果最优,而车辆工作在强非线性区域并处于失稳状态时,PF算法效果最优。 Three-degree freedom vehicle state estimation model is established. Extended Kalman Filter （EKF）, Unscented Kalman Filter （UKF） and Particle Filter （PF） are applied to the estimation of vehicle handling and stability state. Simulation results show that EKE algorithm performs better than both UKF and PF in linear region while PF performs best in nonlinear region.
作者 陈林 施树明 李远方
机构地区 吉林大学交通学院
出处 《交通信息与安全》 2011年第5期36-40,共5页 Journal of Transport Information and Safety
基金 国家自然科学基金项目(批准号:50775094)资助
关键词 操纵稳定性 状态估计 扩展卡尔曼滤波 无迹卡尔曼滤波 粒子滤波 handling and stability state estimation Extended Kalman Filter Unscented Kalman Filter particle filter
分类号 U461.6 [机械工程—车辆工程]
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参考文献12

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二级参考文献77

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共引文献130

同被引文献28

  • 1胡高歌,刘逸涵,高社生,杨一.改进的强跟踪UKF算法及其在INS/GPS组合导航中的应用[J].中国惯性技术学报,2014,12(5):634-639. 被引量：29
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引证文献3

交通信息与安全
2011年 第5期

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