引用本文: 郭士荦, 吴苗, 许江宁, 李京书. 自适应渐消卡尔曼滤波及其在SINS初始对准中的应用[J]. 武汉大学学报 ( 信息科学版), 2018, 43(11): 1667-1672, 1680.
GUO Shiluo, WU Miao, XU Jiangning, LI Jingshu. Adaptive Fading Kalman Filter and Its Application in SINS Initial Alignment[J]. Geomatics and Information Science of Wuhan University, 2018, 43(11): 1667-1672, 1680.
 Citation: GUO Shiluo, WU Miao, XU Jiangning, LI Jingshu. Adaptive Fading Kalman Filter and Its Application in SINS Initial Alignment[J]. Geomatics and Information Science of Wuhan University, 2018, 43(11): 1667-1672, 1680.

## Adaptive Fading Kalman Filter and Its Application in SINS Initial Alignment

• 摘要: 卡尔曼滤波常常被用于惯性导航系统初始对准算法，其使用前提是对系统状态进行建模，从而得到比较准确的系统噪声和观测噪声统计特性。在模型失配和观测噪声干扰的情况下，常规卡尔曼滤波会出现精度下降甚至发散，从而影响初始对准精度。针对这一问题，提出了一种新型渐消卡尔曼滤波算法，引入了多重渐消因子对预测误差协方差阵进行调整，设计了基于新息向量统计特性的滤波状态χ2检验条件，使渐消因子的引入时机更加合理，算法的自适应性得到增强。将改进的卡尔曼滤波算法应用到惯性导航系统的初始对准问题中，仿真试验和实测数据试验结果表明，与常规渐消因子滤波算法相比，新算法可以有效提高滤波精度及鲁棒性。

Abstract: Kalman filter is a common algorithm for initial alignment of inertial navigation system. Its premise is to model the system state and get accurate statistical characteristics of system noise and observation noise. Under the condition of inaccurate model and observation noise, Kalman filter will tend to decline in accuracy or even result in divergence, thus will affect the alignment accuracy. In order to solve this problem, an improved fading Kalman filter is proposed in this paper. Multiple fading factors are introduced to adjust the prediction error covariance matrix, and a Chi-square test method is designed to check the filter state, based on the statistical characteristics of residuals. The introduction of the fading factor is more reasonable, and the algorithm's adaptivity is enhanced. The improved Kalman filter is applied to the initial alignment problem of inertial navigation system. Simulation and real data experiment results show that, the proposed algorithm can improve the accuracy and robustness of Kalman filter effectively.

/

• 分享
• 用微信扫码二维码

分享至好友和朋友圈