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One Practical Data Fusion Algorithm Applied in Auxiliary Particle Filtering

机译:一种实用的辅助粒子滤波数据融合算法

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The filtering for target tracking is discussed in the paper, in order to find a method with better accuracy and reliability than extended kalman(EKF) filtering and unscented kalman filtering(UKF), this paper built a non-linear system model - turn model, then a method of auxiliary particle filters combination with data fusion is proposed. Auxiliary particle filtering for character of the turn model modified every particle before resampling according to likelihood function, which can get the certain accuracy of filtering with fewest particles. In order to improve the filtering result further, it used multi-auxiliary particle filters with a practical data fusion algorithm that is efficient in turn model and can get good result through computer simulation.
机译:本文讨论了用于目标跟踪的滤波,以便找到一种比扩展卡尔曼(EKF)滤波和无味卡尔曼滤波(UKF)更好的准确性和可靠性的方法,本文建立了一个非线性系统模型-转弯模型,然后提出一种辅助粒子滤波与数据融合相结合的方法。针对转向模型特征的辅助粒子滤波,根据似然函数对每个粒子进行重采样,然后进行重采样,可以实现最小粒子滤波的一定精度。为了进一步提高滤波效果,该算法采用了多辅助粒子滤波算法和实用的数据融合算法,该算法有效地进行了建模,并可以通过计算机仿真获得良好的效果。

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