基于修正的自适应平方根容积卡尔曼滤波算法

目标建模不确定性会造成滤波算法性能下降,通过构建强跟踪滤波器(strong tracking filter,STF)可以提升滤波算法的自适应性,但是构建STF时存在理论推导复杂、求解计算量大等局限和不足,针对上述问题,在平方根容积卡尔曼滤波(square-root cubature Kalman filter,SRCK...

Adaptive square-root cubature Kalman filter algorithm based on amending

The uncertainty of target modeling will lead to the performance degradation of the filter algorithm, and the self-adaptability of the filter algorithm can be improved by constructing strong tracking filter (STF). However, there are limitations and deficiencies in the construction of STF, such as complex theoretical derivation and large amount of calculation. To solve the above problems, an adaptive square-root cubature Kalman filter (SRCKF) algorithm based on amending is proposed which is based on SRCKF. By setting judgment threshold and amending rules, the proposed algorithm directly amends the predicted state value or filter gain to balance the proportion of the predicted prior value and the measured posterior feedback value in the filtering, which can reduce the state estimation error. Simulation results show that the algorithm has good filtering performance and numerical stability when the target state is suddenly changed and the measurement is nonlinear. Meanwhile, compared with the STF algorithm which needs to calculate the fading factor, the proposed algorithm has advantages in calculation amount and convergence speed.