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针对滞后无序量测(OOSM)的单步滞后滤波问题,在现有算法的基础上,推导非线性单步滞后无序量测更新方程.提出用UT变换来计算其中涉及到的状态向量以及相关量测之间的协方差,从而有效解决了状态转移方程为线性而量测方程为非线性的非线性Gauss系统的单步滞后OOSM问题.然后,针对多传感器单步滞后OOSM情况,给出了基于UT变换的单步滞后OOSM融合方法.与基于扩展Kalman滤波(EKF)框架下的EKFA1算法和不存在滞后情况的最优算法相比,新算法具有如下特点:可以适用于非线性量测方程的雅可比(Jacobian)矩阵或Hessian矩阵不存在的情况,具有较好的滤波性能,时间复杂度与EKFA1算法处于同一数量级.
Aiming at the one-step lag filtering problem of lag and unordered measurement (OOSM), based on the existing algorithms, the non-linear one-lag unordered measurement updating equation is deduced. The UT transform is used to calculate the state vector involved and Covariance between correlation measurements, so as to effectively solve the single-step lag OOSM problem of the nonlinear Gauss system with a nonlinear state transition equation and a non-linear measuring equation.Then, for single-lag OOSM with multiple sensors, The single-lag OOSM fusion method based on UT transform is compared with the EKFA1 algorithm based on Extended Kalman Filter (EKF) and the optimal algorithm without lag, which has the following characteristics: It can be applied to nonlinear measurement The Jacobian matrix or the Hessian matrix of the equation does not exist, which has good filtering performance and the time complexity is in the same order of magnitude as that of the EKFA1 algorithm.