一种改进后向迭代算法的新型Cubature Kalman滤波器

Wu Bo, Liu Pengyuan
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引用次数: 1

摘要

为了解决非线性机动目标跟踪问题,研究了一种恒加速度模型的三维卡尔曼滤波器(CKF)。仿真结果表明,CKF在跟踪加速度变化剧烈的机动目标时存在时滞过大的问题。为了解决这一问题,在CKF中应用了一种用预测的当前状态修正上次状态估计的后向迭代算法(BI-CKF)。最后,将典型的带转向机动的目标模型应用于CKF和BI-CKF,比较了两种算法的效果。仿真结果表明,BI-CKF算法在动态特性上优于CKF算法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A New Cubature Kalman Filter Improved by Backward Iterative Algorithm
In order to solve the problem of nonlinear maneuvering target tracking, a new cubature kalman filter (CKF) with constant acceleration model was researched. According to simulation result, CKF presented a problem of excessive delay when tracking maneuvering targets with fierce change on acceleration. In order to solve this problem, a backward iterative algorithm that amend the last state estimation with the predicted current state was applied in CKF (BI-CKF). By the end of this paper, a typical target model with turning maneuvering was applied to CKF and BI-CKF, the effect of the two algorithm were compared. The simulation results show that BI-CKF algorithm was better than CKF algorithm at dynamic characteristic.
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