Xu Shuqing, Zhou Haiyin, Wang Jiongqi, He Zhangming, Wang Dayi
College of Liberal Arts and Sciences, National University of Defense Technology, Changsha 410073, China.
Beijing Institute of Spacecraft System Engineering, China Academy of Space Technology, Beijing 100094, China.
Sensors (Basel). 2019 Sep 3;19(17):3812. doi: 10.3390/s19173812.
Among the methods of the multi-source navigation filter, as a distributed method, the federated filter has a small calculation amount with Gaussian state noise, and it is easy to achieve global optimization. However, when the state noise is time-varying or its initial estimation is not accurate, there will be a big difference with the true value in the result of the federated filter. For the systems with time-varying noise, adaptive filter is widely used for its remarkable advantages. Therefore, this paper proposes a federated Sage-Husa adaptive filter for multi-source navigation systems with time-varying or mis-estimated state noise. Because both the federated and the adaptive principles are different in updating the covariance of the state noise, it is required to weight the two updating methods to obtain a combined method with stability and adaptability. In addition, according to the characteristics of the system, the weighting coefficient is formed by the exponential function. This federated adaptive filter is applied to the SINS/CNS/GNSS integrated navigation, and the simulation results show that this method is effective.
在多源导航滤波器的方法中，联合滤波器作为一种分布式方法，在高斯状态噪声下计算量小，且易于实现全局优化。然而，当状态噪声时变或其初始估计不准确时，联合滤波器的结果与真实值会有较大差异。对于具有时变噪声的系统，自适应滤波器因其显著优势而被广泛使用。因此，本文针对状态噪声时变或估计错误的多源导航系统，提出了一种联合Sage-Husa自适应滤波器。由于联合和自适应原理在更新状态噪声协方差时不同，需要对两种更新方法进行加权，以获得一种兼具稳定性和适应性的组合方法。此外，根据系统特性，加权系数由指数函数构成。将这种联合自适应滤波器应用于SINS/CNS/GNSS组合导航中，仿真结果表明该方法是有效的。
