基于卡方检验的自适应鲁棒CKF组合导航算法

Adaptive Robust Cubature Kalman Filter Integrated Navigation Algorithm Based on Chi-square Test

针对组合导航系统在过程噪声统计特性不确定和观测值异常时鲁棒性差的问题,提出了基于卡方检验的自适应鲁棒CKF组合导航算法(CTARCKF)。该算法首先第一次引入卡方检验对系统进行评估,并根据卡方检验值和预设的模糊逻辑函数对过程噪声统计特性进行调节;然后,再次利用卡方检验对观测异常进行判断,并通过增强因子对量测噪声的统计特性进行调节。仿真结果表明,所提出的算法能有效抑制过程噪声变化和观测异常对系统的影响,且在噪声正常和观测值充足的情况下也同样适用。

An adaptive robust CKF integrated navigation algorithm based on chi-square test(CTAR-CKF)was proposed to solve the problem that the integrated navigation system was of poor robustness when the statistical characteristics of process noise were uncertain and observations were abnormal.The algorithm introduced the chi-square test to evaluate the system for the first time,and the statistical characteristics of the process noise were adjusted according to the chi-square test value and the fuzzy logic function.Then,the chi-square test was introduced again to determine whether there was an observation anomaly,and adjust the statistical characteristics of the measurement noise by the enhancement factor.The simulation results showed that the proposed algorithm could effectively suppress the influence of process noise and observation anomaly on the system,and it was also applicable when the noise was normal and the observation value was sufficient.