中文摘要：

在实际运行过程中,往往很难预测目标的运动状态,这就会导致应用在动态导航定位中的卡尔曼滤波算法难以收敛.为了解决这个问题,提出了一种新的自适应卡尔曼滤波算法.该算法采用卡方分布构造统计量,通过检验动力学模型是否出现异常,确定了一种平衡观测方程和状态方程的自适应因子.仿真结果表明,在目标状态发生变化的情况下,该方法优于标准卡尔曼滤波算法和两段函数确定的自适应因子的卡尔曼滤波算法,是-种鲁棒性好、性能可靠、精度高的滤波算法,可广泛应用于动态导航定位中.

英文摘要：

In actual operation process of target, the dynamic motion state of the target is difficult to be predicted, which makes the Kalman filteralgorithm applying in dynamic navigation positioning difficult to converge. In order to solve the problem, a new adaptive Kalman filteringalgorithm is proposed. The chi square distribution is used to construct statistics, through checking if the dynamics model is abnormal or not, theadaptive factor of equilibrium observation equation and state equation is designed. The results of simulation show that in the case of the targetstate charging,this method outperforms the standard Kalman filtering algorithm and the Kalman filtering algorithm thatdetermined by two - segment functions, so it is a kind of filtering algorithm with good robustness, reliable performance and high accuracy, andcan be used widely in dynamic navigation positioning.