简化高阶强跟踪容积卡尔曼滤波及其在组合导航中的应用

针对传统容积卡尔曼滤波(CKF)在面对系统模型失配和状态突变滤波精度下降的问题,将强跟踪滤波器(STF)和高阶容积卡尔曼滤波(HCKF)相结合,提出一种简化高阶强跟踪容积卡尔曼滤波(RHSTCKF)算法.该算法具有比传统CKF更高的滤波精度,并且利用滤波模型的特点,简化HCKF的计算步骤,同时在HCKF中引入多重渐消因子增强算法的自适应性和应对状态突变的能力.将所提算法应用到SINS/GPS组合导航系统中进行仿真实验,结果表明,RHSTCKF可以准确估计出突变状态的真实值,能够抑制滤波器状态异常的干扰,滤波性能明显优于HCKF,能够提高组合导航系统的自适应性和定位精度.

Reduced high-degree strong tracking cubature Kalman filter and its application in integrated navigation system

Focusing on the problem that the filtering precision of the traditional cubature Kalman filter(CKF) decreases under the condition that a system model mismatches and sudden changes of state happen, an algorithm called reduced high-degree strong tracking cubature Kalman filter(RHSTCKF) is proposed, which combines the strong tracking filter with high-degree cubature Kalman filter. The algorithm has higer filtering precision than the traditional CKF, simplifying caculation steps by using the characteristics of the filtering model, and strengthening adaptivity and the ability of sudden changes by introducing the suboptimal multiple fading factor. The proposed algorithm is applied to the SINS/GPS integrated navigation system, and the simulation results show that the RHSTCKF can accurately estimate true value although the state has sudden changes, restrain the interference when filters have abnormal states, obviously improve the filter performance, and enhance the adaptivity and the positional accuracy of the integrated navigation system.