In nonlinear system the unscented Kalman filter (UKF) results often had error or even divergence when the error model was inaccurate or the measurement data contain outliers. So this paper introduced an adaptive robust unscented Kalman filter (ARUKF) algo%针对非线性系统中因噪声模型不准确或测量数据中存在野值而导致无迹卡尔曼滤波(UKF)结果产生偏差甚至发散的问题,提出了一种自适应抗差无迹卡尔曼滤波(ARUKF)算法。该算法利用抗差估计原理构造抗差方差分量统计量,并由该统计量引入自适应因子来调节增益矩阵,减弱野值对滤波的影响。将ARUKF 算法应用于GPS/BD-2组合导航系统中,仿真实验结果表明,当观测数据中存在野值时,ARUKF算法能够有效地控制观测异常误差的影响,定位精度得到了很大提高,并在不同系统噪声和观测噪声方差下,具有较好的鲁棒性和实时性。
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