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针对无人机组合导航滤波算法中传统的运动模型单一固定,灵活性差,无法精确描述无人机复杂多变的运动状态,甚至还会导致滤波发散等问题,提出了一种基于AR运动模型的CKF算法,并应用于无人机导航定位中。通过滑动窗的方法,构建实时动态更新AR模型估计物体运动状态,并且结合CKF进行滤波,从而有效提高无人机导航性能。仿真实验结果表明,该算法能够有效提高无人机导航定位精度,优于其余几种基于传统运动模型的滤波算法。
Aiming at the problems that the traditional motion model of UAV integrated navigation and filtering algorithm is single fixed and has poor flexibility, it can not accurately describe the complex and changeable motion state of UAV, and even lead to the problem of filter divergence. A AR motion model based CKF algorithm, and used in UAV navigation and positioning. Through the sliding window method, real-time dynamic update AR model is constructed to estimate the motion state of the object, and combined with CKF to filter, so as to effectively improve the performance of UAV navigation. Simulation results show that the proposed algorithm can effectively improve the positioning accuracy of the UAV and is superior to the other filtering algorithms based on the traditional motion model.