高阶无迹卡尔曼滤波算法在飞机定位中的应用  

APPLICATION OF HIGH-ORDER UNSCENTED KALMAN FILTER IN AIRPLANE LOCALISATION

作　　者：刘家学[1] 林松岩 

机构地区：[1]中国民航大学航空自动化学院,天津300300

出　　处：《计算机应用与软件》2016年第5期256-259,264,共5页Computer Applications and Software

基　　金：民航局科技基金项目(MHRD201121);中央高校基本科研业务费项目(ZXH2012D015;ZXH2012G004)

摘　　要：无迹卡尔曼滤波算法(UKF)在飞机定位和跟踪的过程中精度不够,原因在于误差变量的偏度和峰态在坐标转换过程中对其分布影响很大。为了解决这一问题,将高阶无迹卡尔曼滤波算法应用到QAR数据中。首先,根据高阶UT变换,选取一组样本点(sigma点)表征k时刻最优估计值前四阶矩的分布特征,通过传递得到k+1时刻一步预测值的先验概率分布。然后以观测数据作为量测值,带入滤波算法得到k+1时刻飞机状态的最优估计值。最后根据计算机产生的模拟噪声数据和真实的QAR数据实现飞机定位的仿真验证。从仿真结果看,高阶无迹卡尔曼滤波算法比无迹卡尔曼滤波精度更高,误差更小,对QAR数据中其他类型的数据形式有一定的借鉴意义。The cause of inadequate accuracy of unscented Kalman filter（ UKF） in localising and tracing an airplane is due to the very big influence of skewness and kurtosis of error variables on the distribution of coordinate during its transformation process. In order to solve the problem,we applied high-order UKF algorithm to quick access recorder（ QAR） data. First,according to high-order unscented transformation（ UT） we chose a set of sample points（ sigma points） to characterise the distribution feature of the first four moments of optimal estimating value at time k,and obtained the priori probability distribution of one-step prediction value at time k ＋ 1 through transferring. Then we took the observation data as the measured value,and brought in the filtering algorithm to get the optimal estimation value of airplane state at time k ＋ 1. At last,according to computer-generated simulative noise data and actual QAR data we achieved the simulated validation of airplane localisation. From the simulation result it is aware that the high-order UKF algorithm has higher accuracy and less error than UKF,this has certain reference significance to the data form of other types in QAR data.

关 键 词：QAR数据 高阶UT变换 高阶无迹卡尔曼滤波 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]