基于改进的迭代容积卡尔曼滤波姿态估计

为了充分利用新的量测信息,提高姿态估计的精度,在分析现有迭代滤波策略存在问题的基础上,采用一种新的容积点迭代策略,将其与容积卡尔曼滤波算法相结合,提出了一种改进的迭代容积卡尔曼滤波(improved iterated cubature Kalman filter, IICKF)算法．该算法采用容积数值积分理论近似非线性函数的均值与方差,利用状态扩维理论来解决量测迭代中量测噪声与状态相关的问题,同时利用一种新的容积点迭代策略,即在量测迭代过程中直接采用容积点迭代,避免每步迭代都进行均方根计算来产生容积点,克服传统迭代策略是基于高斯近似产生采样点的局限,有效地降低扩维带来的计算量．仿真结果表明：该算法的估计精度高于乘性扩展卡尔曼滤波(multiplicative extended Kalman filter, MEKF)以及迭代容积卡尔曼滤波(iterated cubature Kalman filter, ICKF)算法,该算法的提出有助于提高姿态估计的精度．

Attitude estimation based on improved iterated cubature Kalman filter

To make use of the latest measurement information sufficiency, and to improve the accuracy of attitude estimation, based on the analysis of the current iterated filtering strategy, an improved iterated cubature Kalman filter(IICKF) is presented in this paper by combining a new cubature points iterated strategy with cubature Kalman filter. The filtering algorithm uses the cubature numerical integration theory to calculate the mean and variance of the nonlinear function, utilizing the state augmented method to solve the issue that the state is correlated with the measurement noise in the iterated process. A new cubature points iterated strategy is developed, which can directly iterate the cubature points, and thus avoids to generate cubature points by calculating the mean-squared root. It overcomes the limitation that sampling points are produced by the Gauss approximation in the traditional iterative strategy, which can reduce computational complexity. Simulation results show that IICKF is superior to multiplicative extended Kalman filter and iterated cubature Kalman filter in precision, which indicates that it can help to improve the accuracy of attitude estimation.