摘要
为了进一步提高捷联惯性测试系统的精度，提出了基于卡尔曼数据融合技术的铁路轨道几何参数捷联惯性测试方法．在分析影响捷联惯性测试系统精度主要误差因素的基础上，建立了反映测试系统误差影响因素的状态空间方程，分析了外部辅助观测量的选取及其对卡尔曼滤波系统可观测性和可观测度的影响．以速度、侧滚角作为外部辅助观测量，并且以2阶自回归模型对随机振动干扰进行实时建模，仿真计算结果表明，相较于普通的卡尔曼滤波算法，该方法可以使卡尔曼滤波的估计精度显著提高，俯仰角和航向角误差估计值的标准偏差均减小1．5°～2．5°．
To further increase the accuracy of strapdown inertial measurement system, based on Kalman data fusion technique, a novel strapdown inertial measuring method for railway track geometrical parameters was proposed. By analyzing major error factors that affect the strapdown inertial system, the error state space equations were constructed. The selection of external observations and their influences on the observability and the degree of observability of the Kalman filter were also analyzed. The velocity and roll angle were used as external observations, and the random vibration interference was modeled with second-order auto-regression equations. The simulation results show that compared with the common Kalman filtering algorithm, the proposed method can greatly increase the estimation accuracy of Kalman filtering state variables, and the standard deviation of pitch angle and azimuth angle is decreased by 1.5° to 2.5 °.
出处
《西南交通大学学报》
EI
CSCD
北大核心
2014年第1期8-14,51,共8页
Journal of Southwest Jiaotong University
基金
中央高校基本科研业务费专项资金资助项目(SWJTU11CX021)
关键词
捷联惯性系统
卡尔曼滤波
随机误差
实时建模
误差补偿
strapdown inertial system
Kalman filtering
random errors
real time modeling
errorcompensation
