行军车辆惯导系统初始对准两种方法的比较

Chen Hong-yue, Sun Qian
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引用次数: 0

摘要

经典卡尔曼滤波算法对初始值要求较精确,但行军车辆惯导系统的粗对准精度不能保证满足所有工况的要求。在距离传递单元(DTU)的辅助下,提出了unscented卡尔曼滤波(UKF)来实现行军车辆捷联惯导系统的初始对准。阐述了大不对准角情况下误差模型的系统方程,推导了以捷联惯导系统提供的速度与DTU计算的速度之差作为测量变量时的观测方程,并对小不对准角情况下的误差模型进行了简化。详细阐述了基于加性噪声模型的UKF算法。基于车辆试验数据的仿真表明,UKF算法能够实现行进车辆的初始对准,并且比经典卡尔曼滤波算法对初始值具有更强的鲁棒性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Comparison of Two Approaches to SINS Initial Alignment for Marching Vehicle
Classic Kalman filtering algorithm needs a little precise initial value, but coarse alignment precision of SINS for marching vehicle can't ensure meet the requirement in all working conditions. UKF(unscented kalman filter) was proposed to achieve initial alignment of SINS for marching vehicle with DTU(distance transfer unit) aiding. The system equation of error model based on large misalignment angles was expounded, observation equation was derived when the measurement variables were chosen as difference of velocity offered by SINS and velocity reckoned by DTU, and error model was simplified under the situation of small misalignment angles. UKF algorithm based on additive noise model was expounded detailed. Simulation based on vehicular tests data showed that UKF algorithm could achieve initial alignment for marching vehicle, and is more robust to initial value than classic kalman filtering algorithm.
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