Space Control and Inertial Technology Research Center, Harbin Institute of Technology, Harbin 150080, China.
Sensors (Basel). 2018 Jun 28;18(7):2081. doi: 10.3390/s18072081.
For a running freely land-vehicle strapdown inertial navigation system (SINS), the problems of self-calibration and attitude alignment need to be solved simultaneously. This paper proposes a complete alignment algorithm for the land vehicle navigation using Inertial Measurement Units (IMUs) and an odometer. A self-calibration algorithm is proposed based on the global observability analysis to calibrate the odometer scale factor and IMU misalignment angle, and the initial alignment and calibration method based on optimal algorithm is established to estimate the attitude and other system parameters. This new algorithm has the capability of self-initialization and calibration without any prior attitude and sensor noise information. Computer simulation results show that the performance of the proposed algorithm is superior to the extended Kalman filter (EKF) method during the oscillating attitude motions, and the vehicle test validates its advantages.
对于一个自由运行的车载捷联惯性导航系统（SINS），需要同时解决自校准和姿态对准问题。本文提出了一种基于惯性测量单元（IMU）和里程计的完整的陆地车辆导航对准算法。基于全局可观性分析，提出了一种自校准算法来校准里程计比例因子和 IMU 失准角，并建立了基于最优算法的初始对准和校准方法来估计姿态和其他系统参数。这种新算法具有无需任何先验姿态和传感器噪声信息的自初始化和校准能力。计算机仿真结果表明，在振荡姿态运动中，所提出的算法的性能优于扩展卡尔曼滤波（EKF）方法，并且车辆测试验证了其优势。
