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| 利用预测残差和选权滤波构造的分类因子在GPS/INS组合导航中的应用 | |
| 引用本文： | 吴富梅,聂建亮,何正斌.利用预测残差和选权滤波构造的分类因子在GPS/INS组合导航中的应用[J].武汉大学学报(信息科学版),2012,37(3):261-264. |
| 作者姓名： | 吴富梅 聂建亮 何正斌 |
| 作者单位： | 1. 信息工程大学测绘学院,郑州市陇海中路66号450052／西安测绘研究所,西安市雁塔中路1号710054 2. 国家测绘地理信息局大地测量数据处理中心,西安市友谊东路334号,710054 3. 长安大学地质工程与测绘学院,西安市雁塔路126号,710054 |
| 基金项目： | 国家自然科学基金资助项目，卫星导航与定位教育部重点实验室开放基金资助项目 |
| 摘 要： | 针对组合导航观测个数少、采用单因子自适应滤波会损失间接可测参数精度的问题,利用预测残差和选权滤波思想构造了分类自适应因子。实测算例计算结果表明,该算法不仅能够很好地控制状态扰动异常影响,而且还能避免损失间接可测参数的精度,进一步提高了导航精度。 |
| 关 键 词： | 预测残差 选权滤波 分类因子 GPS/INS |
Classified Adaptive Filtering to GPS/INS Integrated Navigation Based on Predicted Residuals and Selecting Weight Filtering |
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| WU Fumei,NIE Jianliang,HE Zhengbin.Classified Adaptive Filtering to GPS/INS Integrated Navigation Based on Predicted Residuals and Selecting Weight Filtering[J].Geomatics and Information Science of Wuhan University,2012,37(3):261-264. | |
| Authors: | WU Fumei NIE Jianliang HE Zhengbin |
| Institution: | 1 Institute of Surveying and Mapping,Information Engineering University,66 Middle Longhai Road,Zhengzhou 450052,China)(2 Xi’an Research Institute of Surveying and Mapping,1 Middle Yanta Road,Xi’an 710054,China)(3 Geodetic Data Processing Center,National Administration of Surveying,Mapping and Geoinformation, 334 East Youyi Road,Xi’an 710054,China)(4 School of Geological and Surveying Engineering,Chang’an University,126 Yanta Road,Xi’an 710054,China) |
| Abstract: | In GPS/INS integrated navigation,the number of observations is usually less than that of the state parameters,and the single adaptive factor is usually applied in Kalman filtering,which can lead to precision loss of indirect observational parameters.A new algorithm of classified adaptive filtering is presented based on predicted residuals and selecting weight filtering,and the corresponding formulas are given.Finally,an actual calculation is given.The new algorithm can not only degrade the influence of the disturbances from the state but also avoid the loss of estimated precision of indirect observational parameters,and improve the accuracy of the navigation further. |
| Keywords: | predicted residual selecting weight filtering classified factors GPS/INS |
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