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。
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.
出处
《武汉大学学报(信息科学版)》
EI
CSCD
北大核心
2010年第2期156-159,共4页
Geomatics and Information Science of Wuhan University
基金
国家自然科学基金资助项目(40774001
40841021
40604003)
国家863计划资助项目(2007AA12Z331)
卫星导航与定位教育部重点实验室(B类)开放研究基金资助项目
关键词
GPS/INS紧组合导航
自适应滤波
预测残差
部分状态不符值
tight coupled GPS/INS integration
adaptive filtering
predicted residual
part state discrepancy statistic