基于抗差自适应容积卡尔曼滤波的超紧耦合跟踪方法

摘要

To reduce the negative impact existing in the ultra-tightly coupled structure based on single-modulating loop, an ultra-tightly coupled structure based on double-modulating loop is designed. Aiming at overcoming the nonlinear filter problem of the tracking loop in the designed structure, an ultra-tightly coupled tracking method using a robust adaptive cubature Kalman filter (RACKF) algorithm is proposed to cover measurement outliers and kinematic model errors. The proposed RACKF algorithm adopts the robust M estimation to adjust cubature Kalman filter (CKF) algorithm so as to detect and reduce the influence“degree”of measurement outliers. At the same time, RACKF adopts an adaptive factor to dispose the influence introduced by dynamic disturbance errors. Experiment results show that the proposed method can effectively resist errors aroused by inaccurate model, and that the high-accuracy and stably tracking for GPS satellite signal are achieved preferably. As revealed by comparative studies, the proposed method is superior to tracking method based on single-modulating loop in tracking performance. Moreover, the proposed method has a higher tracking performance than tracking methods based on unscented Kalman filter (UKF) and CKF, and thus it improves the applicability of the navigation system under the circumstance of high dynamics.%为降低基于单一调节回路的超紧耦合结构存在的反作用影响,设计了一种基于双回路的超紧耦合结构。基于此,为解决所设计结构中跟踪环路的非线性滤波问题,针对测量异常误差和动力学模型误差,提出了一种基于抗差自适应容积卡尔曼滤波(Robust adaptive cubature Kalman filter, RACKF)的超紧耦合跟踪算法。该算法采用稳健M 估计调整容积卡尔曼滤波(Cubature Kalman filter, CKF)算法,对观测量中粗差的影响“程度”进行探测和处理,以减小观测量异常误差产生的影响,同时利用自适应调节因子对算法进行调节修正,以处理动态扰动误差引入的影响。实验结果表明：所提出的方法能有效地处理模型不准确所引入的误差,较好地实现全球定位系统(Global positioning system, GPS)卫星信号的高精度和稳定跟踪,其跟踪性能远优于基于单一回路的跟踪方法,同时优于基于无迹卡尔曼滤波(Unscented Kalman filter, UKF)和基于CKF的跟踪方法,提升了导航系统在高动态条件下的适应性能。