Rong Hanxiao, Gao Yanbin, Guan Lianwu, Zhang Qing, Zhang Fan, Li Ningbo
Collage of Automation, Harbin Engineering University, Harbin 150001, China.
Sensors (Basel). 2019 Aug 15;19(16):3564. doi: 10.3390/s19163564.
To solve the self-alignment problem of the Strapdown Inertial Navigation System (SINS), a novel adaptive filter based on Complementary Ensemble Empirical Mode Decomposition (CEEMD) is proposed. The Gravitational Apparent Motion (GAM) is used in the coarse alignment, and the problem of obtaining the attitude matrix between the body frame and the navigation frame is attributed to obtaining the matrix between the initial body frame and the current navigation frame using two gravitational apparent motion vectors at different moments. However, the accuracy and time of this alignment method always suffer from the measurement noise of sensors. Thus, a novel adaptive filter based on CEEMD using an l 2 -norm to calculate the similarity measure between the Probability Density Function (PDF) of each Intrinsic Mode Function (IMF) and the original signal is proposed to denoise the measurements of the accelerometer. Furthermore, the advantage of this filter is verified by comparing with other conventional denoising methods, such as PDF-based EMD (EMD-PDF) and the Finite Impulse Response (FIR) digital low-pass filter method. The results of the simulation and experiments indicate that the proposed method performs better than the conventional methods in both alignment time and alignment accuracy.
为解决捷联惯性导航系统（SINS）的自对准问题，提出了一种基于互补总体经验模态分解（CEEMD）的新型自适应滤波器。在粗对准中采用重力视运动（GAM），将获取机体坐标系与导航坐标系之间姿态矩阵的问题归结为利用不同时刻的两个重力视运动矢量获取初始机体坐标系与当前导航坐标系之间的矩阵。然而，这种对准方法的精度和时间总是受到传感器测量噪声的影响。因此，提出了一种基于CEEMD的新型自适应滤波器，利用l2范数计算各本征模态函数（IMF）的概率密度函数（PDF）与原始信号之间的相似性度量，对加速度计的测量值进行去噪。此外，通过与其他传统去噪方法（如基于PDF的经验模态分解（EMD-PDF）和有限脉冲响应（FIR）数字低通滤波器方法）进行比较，验证了该滤波器的优势。仿真和实验结果表明，所提方法在对准时间和对准精度方面均优于传统方法。
