自适应Kalman滤波在SINS初始对准中的应用

Application of adaptive Kalman filter technique in initial alignment of inertial navigation system

为了提高捷联惯导系统的对准精度和收敛速度,提出了一种基于Sage-Husa自适应滤波算法的初始对准方法。针对方位小失准角的情况,推导出精对准误差模型和自适应Kalman滤波方程。常规Kalman滤波算法,在噪声统计特性已知的情况下,使用比较方便;多数情况下,噪声统计特性是处于未知状态,从而引入自适应Kalman滤波算法。它利用观测到的数据自动进行噪声统计特性的在线估计和修正,使系统达到最佳的滤波效果。通过仿真验证,该自适应滤波算法有效地提高了收敛速度和对准精度。

To improve the alignment precision and convergence speed of strap-down inertial navigation system, an initial alignment method based on Sage-Husa adaptive filter is proposed. The exactitude alignment error model and the adaptive Kalman filter equation are derived for the case of small misalignment azimuth angle. When the noise statistical characteristics are known, the Kalman filter is suitable; while in the active system, most noise statistical characteristics are unknown, so we introduce an adaptive filter. It use the information of observations on-line estimation and correct the noise statistical characteristics, makes the system has a higher filtering effect. Through simulation verifiable, the adaptive Kalman filter algorithm, improve the convergence speed and alignment accuracy effectively.