基于SVD的推广卡尔曼滤波及其在飞行状态和参数估计中的应用

A SVD-Based Extended Kalman Filter and Application to Flight State and Parameter Estimation of Aircraft

中文关键词  推广卡尔曼滤波  奇异值分解  状态和参数估计  飞行实验

英文关键词  extended Kalman filter  singular value decomposition  state and parameter estimation  flight test

中文摘要

      基于矩阵的奇异值分解（SVD)技术，本文提出一种鲁棒推广卡尔曼滤波新算法，并将该算法应用于飞行状态和参数估计中，该算法不仅具有很好的数值稳定性，而且无需任何变换即可处理相关噪声，且适于并行计算.两种不同型号飞机飞行状态和参数估计的仿真及实际试飞数据计算结果表明；与EKF相比，本文算法对不同初始值的不同噪声均可获得更准确的估计结果，并且对飞机机动形式、噪声水平、数据长度等要求不高，收敛性好.利用系统和量测模型的一些特点及对奇异值分解算法的改进，使算法计算量大大减少.

英文摘要

      In this paper，a new robust extended Kalman filtering algorithm based on singular value decomposition (SVD) of covariance information matrix is presented with application to the flight state and parameter estimation of aircraft. The presented algorithm not only has a good numerical stability but also can handle correlated measurement noise without any additional transformation. The algorithm is formulated in the form of vector-matrix operations，so it is also useful for parallel computers. The applications to the flight state and parameter estimation by simulated and actual flight test data computation of two types of Chinese aircraft show that the new algorithm presented in this paper can give more accurate estimates of flight state and parameter than extended Kalman filter (EKF) for different initial values and noise statistics.Moreover，the new algorithm has less requirements for the maneuvering shapes,noise levels，data length and better convergency than those of EKF. The computational requirements of the new filtering algorithm have been reduced greatly by exploiting some special features of matrix computation and system model. It is proved that the new filtering algorithm can give good results even for low sample rate flight test data.