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机构地区:[1]沈阳工业大学大学信息科学与工程学院,沈阳110870 [2]辽宁大学物理学院,沈阳110036 [3]中国民用航空局东北地区空中交通管理局,沈阳110043
出 处:《沈阳工业大学学报》2013年第6期687-691,共5页Journal of Shenyang University of Technology
基 金:国家自然科学基金仪表专项资助项目(60927004);国家科技部支撑计划资助项目(2011BAK06B01-03)
摘 要:针对管道地理坐标测量系统低精度惯性测量单元(inertial measurment unit,IMU)初始对准问题,提出一种管道定位的初始对准方法.针对大角度情况进行粗对准,然后在小角度范围内再进行精对准.在精对准过程中,对测量信号采用一阶马尔科夫模型,提取出符合kalman滤波模型的白噪声分量,随后建立13维状态量的kalman状态方程以及观测方程.对于较大方位误差角引起的非线性,采用Cubature Kalman滤波(Cubature Kalman filtering,CKF)算法对非线性模型进行状态估计,解决了滤波模型的非线性问题并进行了静态对准实验.实验结果表明,设计的算法在计算时间上优于Unscented粒子滤波(Unsented particle filtering,UPF)算法,适于作为管道地理坐标测量的初始对准算法.In oder to solve the initial alignment problem for the inertial measurment unit (IMU) with low accuracy, an initial alignment method for the positioning of pipeline was proposed. Firstly, the coarse alignment for the large angle situation was carried out. Then, the precise alignment within the range of small angles was performed. In the process of precise alignment, the first order Markov model was adopted for the measurement signals, and the white noise component suitable for the Kalman filtering model was extracted. In addition, the Kalman state and observation equations with 13-dimensional state variables were established. For the nonlinear problem caused by the larger attitude error angles, the state estimation of nonlinear model was conducted with the Cubature Kalman filtering (CKF) algorithm, and the nonlinear problems of filtering model were solved. The static alignment experiment was carried out. The experimental results show that the designed algorithm is superior to that of unsented particle filtering (UPF) algorithm in the calculating time, and can be suitable as the initial alignment algorithm for the geographical coordinate measurement of pipeline.
关 键 词:捷联惯导 初始对准 地理坐标测量 姿态角 一阶马尔科夫 惯性测量单元 Unscented粒子滤波 CUBATURE KALMAN滤波
分 类 号:TP274[自动化与计算机技术—检测技术与自动化装置]
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