【摘要】 提出了一种通过部分状态不符值来构造自适应因子的方法。实测算例结果表明,当观测无异常时,由预测残差构造的自适应因子和由部分状态不符值构造的自适应因子都能够较好地抑制动态模型误差的影响,相比于标准Kalman滤波精度都有所提高,并且这两种自适应滤波的精度相当;但是当观测存在异常时,由预测残差构造的自适应因子不能分辨模型误差和观测误差,而由部分状态不符值构造的自适应因子能够抵制观测异常的影响,因此,滤波结果优于由预测残差构造的自适应因子的滤波结果。更多还原

【Abstract】 A new factor based on partial state discrepancy was developed.Compared with the standard Kalman filtering,both the adaptive factors constructed by the predicted residuals and partial state discrepancy can resist the influence of the dynamic model errors when no outliers exist in measurements.The precisions of their navigation are almost idential.But if outliers exist in measurements,the adaptive factor based on the predicted residuals can not identify the state model errors and the observation errors while the adaptive factor based on partial state discrepancy can resist the influence of the outliers.Hence,the latter navigation precision is prior to the former navigation precision.更多还原