Liu Yiting, Xu Xiaosu, Liu Xixiang, Yao Yiqing, Wu Liang, Sun Jin
Key Laboratory of Micro-Inertial Instrument and Advanced Navigation Technology, Ministry of Education, School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China,.
Sensors (Basel). 2015 Apr 27;15(5):9827-53. doi: 10.3390/s150509827.
Initial alignment is always a key topic and difficult to achieve in an inertial navigation system (INS). In this paper a novel self-initial alignment algorithm is proposed using gravitational apparent motion vectors at three different moments and vector-operation. Simulation and analysis showed that this method easily suffers from the random noise contained in accelerometer measurements which are used to construct apparent motion directly. Aiming to resolve this problem, an online sensor data denoising method based on a Kalman filter is proposed and a novel reconstruction method for apparent motion is designed to avoid the collinearity among vectors participating in the alignment solution. Simulation, turntable tests and vehicle tests indicate that the proposed alignment algorithm can fulfill initial alignment of strapdown INS (SINS) under both static and swinging conditions. The accuracy can either reach or approach the theoretical values determined by sensor precision under static or swinging conditions.
初始对准始终是惯性导航系统（INS）中的一个关键课题，且难以实现。本文提出了一种利用三个不同时刻的重力视运动矢量和矢量运算的新型自对准算法。仿真与分析表明，该方法容易受到加速度计测量中所含随机噪声的影响，而这些测量数据直接用于构建视运动。为解决这一问题，提出了一种基于卡尔曼滤波器的在线传感器数据去噪方法，并设计了一种新型视运动重构方法，以避免参与对准解算的矢量共线。仿真、转台测试和车辆测试表明，所提出的对准算法能够在静态和摇摆条件下完成捷联惯性导航系统（SINS）的初始对准。在静态或摇摆条件下，精度均可达到或接近由传感器精度确定的理论值。
