The introduction of the full paper reviews a number of relevant papers in the open literature, points out what we believe to be their shortcomings, and then proposes a new method mentioned in the title, which we believe is more efficient than previous ones and which is explained in sections 1,2, and 3. Their core consists of; "Theories of random weighting estimation are established to estimate the kinematic model error and covariance matrices of predicted state vector for controlling the disturbances on state parameter estimation due to the kinematic model noise. This method incorporates random weights into the kinematic model error and the covariance matrix of predicted state vector to control the magnitudes of the covariance matrices of residual vectors and predicted residual vectors for weakening the disturbance of the kinematic model error on the state parameter estimation, thus improving the filtering accuracy for dynamic positioning and navigation. " Simulation results, presented in Figs. 1,2, and 3 , and Table 1, and their analysis demonstrate preliminarily that, compared with existing methods, the proposed method can suppress indeed more efficiently the disturbances due to the kinematic model noise, thus significantly improving the positioning accuracy for dynamic navigation.%提出用新兴的随机加权估计算法对动力学模型系统误差进行估计,以控制动态模型噪声异常对状态参数估值的影响.该算法根据实际需要对动力学模型误差和状态预报值向量的协方差阵进行随机加权,以控制残差向量协方差阵和新息向量协方差阵的大小,削弱模型误差对状态参数向量的影响,提高动态导航解算的滤波精度.仿真结果表明,提出的随机加权估计算法的估计精度,明显优于经典Kalman滤波和抗差自适应Kalman滤波,能提高飞行器动态导航定位的精度.
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