研究了三维系统偏差条件下的扩维目标跟踪问题,提出了一种基于不敏卡尔曼滤波器(UKF)的系统偏差和目标状态的联合估计算法(ASUKF).Monte-Carlo仿真结果表明,ASUKF算法较好地避免了扩展卡尔曼滤波器的模型线性化误差易导致滤波发散的问题,能更加有效地对目标状态和系统偏差进行实时联合估计.
The problem that how to track a three-dimensional target with systematic errors is researched in this paper. Using the unscented Kalman filter, an augmented state unscented Kalman filter tracking algorithm is proposed. The simulation results show that the given algorithm does not require the linearization of the model, and can estimate efficiently.