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摘要: 为克服多传感器信息融合时联邦Kalman滤波在系统噪声和测量噪声的统计信息不准 确时所存在的局限性,提出了一种基于最小二乘估计的新型联邦滤波算法,定义为联邦最小二乘 滤波.定性讨论了它与联邦Kalman滤波的关系,通过在INS/双星/GPS组合导航系统中的 实际应用进一步地比较两种算法.实测数据的仿真结果证明,在系统噪声和测量噪声不准确的情 况下,联邦最小二乘滤波的精度要高于联邦Kalman滤波.Abstract: A new type of federated filtering based on the least square estimation is presented, which is defined as federated least square filtering (FLSF), while the statistic information of the system noise and observation noise are uncertain in order to overcome the limitation of federated Kalman filtering (FKF) in rnulti-sensor information fusion. The relationship between FLSF and FKF is discussed in some detail. These two methods are compared further for practical application in inertial navigation system/double-star system/global positioning system (INS/DS/GPS) integrated navigation system. The simulating results indicate that the FLSF has better filtering accuracy than FKF, while the statistic information of the system noise and observation noise are uncertain.
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Key words:
- Integrated navigation /
- least square estimation /
- Kalman filtering /
- federated filtering
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