一种针对大失准角捷联惯导系统初始对准的处理方法

A Processing Method for Strapdown Inertial Navigation System Initial Alignment Under Large Misalignment Angles

基于平台误差角概念,在欧拉旋转角的理论基础上推导了大失准角状态下的非线性捷联惯导系统(strapdown inertial navigation system,SINS)初始对准误差模型。模型中,将系统状态噪声和量测噪声均视为复杂加性噪声。将量测方程视为线性方程,对经典无迹卡尔曼滤波(unscented Kalman filter,UKF)算法进行简化,有效地降低了滤波算法复杂性与计算量。针对所提出的非线性SINS初始对准的误差模型和简化后的UKF滤波算法进行实验验证,结果表明,在惯导设备精度较高的情况下,算法能够有效应对多类失准角问题;在惯导设备精度很低时,水平角能得到准确对准,方位角无法有效对准。

Based on the concept of platform error angle, the nonlinear initial alignment error model of strapdown inertial navigation system(SINS) under large misalignment angle is derived on the theoretical basis of Euler rotation angle. In this model, both state noise and measurement noise are considered complex additive noise. In the case that the measurement equation is considered as a linear equation, the unscented kalman filter(UKF) algorithm is simplified, which effectively reduces the filtering complexity and calculation. Experiments are made to verify SINS nonlinear initial alignment error model and simplified UKF filtering algorithm. Results show that the algorithm can effectively deal with many types of misalignment angle problems in the case of high precision of inertial navigation equipment. When the accuracy of inertial navigation equipment is low, the horizontal angle can be accurately aligned and the azimuth cannot be effectively aligned.