Zhu Tianlong, Li Jian, Duan Kun, Sun Shouliang
College of Information Science and Engineering, Hohai University, Changzhou 213001, China.
China Mobile Communications Corporation Shandong Company Ltd., Jinan 250000, China.
Sensors (Basel). 2024 Oct 13;24(20):6596. doi: 10.3390/s24206596.
This paper proposes an improved adaptive filtering algorithm based on the Sage-Husa adaptive Kalman filtering algorithm to address the issue of measurement noise characteristics impacting the navigation accuracy in strapdown inertial navigation system (SINS)/Doppler Velocity Log (DVL) integrated navigation systems. Addressing the non-positive definite matrix problem prevalent in traditional adaptive filtering algorithms and aiming to enhance measurement noise estimation accuracy, this method incorporates upper and lower thresholds determined by a discrimination factor. In the presence of abnormal measurement data, these thresholds are utilized to adjust the covariance of the innovation, subsequently re-estimating the system's measurement noise through a decision factor based on the innovation. Simulation and experiment results demonstrate that the proposed improved adaptive filtering algorithm outperforms the classical Kalman filter (KF) in terms of navigation accuracy and stability. Furthermore, the filtering performance surpasses that of the Sage-Husa algorithm. The simulation results in this paper show that the relative position positioning error of the improved method is reduced by 49.44% compared with the Sage-Husa filtering method.
本文提出了一种基于Sage-Husa自适应卡尔曼滤波算法的改进自适应滤波算法，以解决捷联惯性导航系统（SINS）/多普勒速度计（DVL）组合导航系统中测量噪声特性影响导航精度的问题。针对传统自适应滤波算法中普遍存在的非正定矩阵问题，为提高测量噪声估计精度，该方法引入了由判别因子确定的上下阈值。当存在异常测量数据时，利用这些阈值调整新息协方差，随后通过基于新息的决策因子重新估计系统的测量噪声。仿真和实验结果表明，所提出的改进自适应滤波算法在导航精度和稳定性方面优于经典卡尔曼滤波器（KF）。此外，其滤波性能超过了Sage-Husa算法。本文仿真结果表明，与Sage-Husa滤波方法相比，改进方法的相对位置定位误差降低了49.44%。
