School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150001, China.
Sensors (Basel). 2018 Sep 30;18(10):3297. doi: 10.3390/s18103297.
Along with the development of computer technology and informatization, the unmanned vehicle has become an important equipment in military, civil and some other fields. The navigation system is the basis and core of realizing the autonomous control and completing the task for unmanned vehicles, and the Strapdown Inertial Navigation System (SINS) is the preferred due to its autonomy and independence. The initial alignment technique is the premise and the foundation of the SINS, whose performance is susceptible to system nonlinearity and uncertainty. To improving system performance for SINS, an improved initial alignment algorithm is proposed in this manuscript. In the procedure of this presented initial alignment algorithm, the original signal of inertial sensors is denoised by utilizing the improved signal denoising method based on the Empirical Mode Decomposition (EMD) and the Extreme Learning Machine (ELM) firstly to suppress the high-frequency noise on coarse alignment. Afterwards, the accuracy and reliability of initial alignment is further enhanced by utilizing an improved Robust Huber Cubarure Kalman Filer (RHCKF) method to minimize the influence of system nonlinearity and uncertainty on the fine alignment. In addition, real tests are used to verify the availability and superiority of this proposed initial alignment algorithm.
随着计算机技术和信息化的发展，无人车已经成为军事、民用和其他一些领域的重要设备。导航系统是实现无人车自主控制和完成任务的基础和核心，而捷联惯性导航系统（SINS）由于其自主性和独立性而成为首选。初始对准技术是 SINS 的前提和基础，其性能容易受到系统非线性和不确定性的影响。为了提高 SINS 的性能，本文提出了一种改进的初始对准算法。在提出的初始对准算法中，首先利用基于经验模态分解（EMD）和极限学习机（ELM）的改进信号去噪方法对惯性传感器的原始信号进行去噪，以抑制粗对准过程中的高频噪声。然后，利用改进的鲁棒 Huber Cubature Kalman 滤波器（RHCKF）方法进一步提高初始对准的精度和可靠性，以最小化系统非线性和不确定性对精对准的影响。此外，还利用实际测试验证了所提出的初始对准算法的有效性和优越性。
