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Application of Adaptive Kalman Filter Technique in Initial Alignment of Strapdown Inertial Navigation System

机译:自适应卡尔曼滤波技术在捷联惯导系统初始对准中的应用

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In order 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 presented. We also derived the exactitude alignment error model and adaptive Kalman filter equation in the azimuth of small misalignment angle. As usual, known the noise statistical characteristics, Kalman filter is suitable; but in the active system most noise statistical characteristics are unknown, in this case, we introduce the adaptive Kalman filter. It uses the information of observed data, on-line estimation noise statistical characteristics and state simultaneously in order to improve the filter continuously, so, the filter has a higher estimation accuracy than the conventional Kalman filter. By simulating verifying, the adaptive Kalman filter enhances the convergence speed and alignment accuracy effectively.
机译:为了提高捷联惯性导航系统的对准精度和收敛速度,提出了一种基于Sage-Husa自适应滤波器的初始对准方法。我们还推导了小失准角方位角上的精确度对准误差模型和自适应卡尔曼滤波方程。像往常一样,已知的噪声统计特性,卡尔曼滤波器是合适的;但是在有源系统中,大多数噪声统计特性是未知的,在这种情况下,我们引入了自适应卡尔曼滤波器。它同时使用观测数据信息,在线估计噪声统计特性和状态,以不断改进滤波器,因此,与传统的卡尔曼滤波器相比,该滤波器具有更高的估计精度。通过仿真验证,自适应卡尔曼滤波器有效提高了收敛速度和对准精度。

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