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.