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Comparison of Angle-only Filtering Algorithms in 3D Using EKF, UKF, PF, PFF, and Ensemble KF

机译:使用EKF,UKF,PF,PFF和Ensemble KF比较3D中的唯一滤波算法

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In our previous work, we compared the performance of the extended Kalman filter (EKF), unscented Kalman filter (UKF), and particle filter (PF) for the angle-only filtering (AOF) problem in 3D using Cartesian coordinates and modified spherical coordinates (MSC) for the relative state vector. We found that the UKF-MSC and EKF-MSC had the best performance in accuracy, the UKF-MSC being slightly better than the EKF-MSC. The PF didn't perform well compared with the EKF and UKF and had a higher computational cost. In this work, we compare the performance of the particle flow filter (PFF) with the other filters for the AOF problem. In addition, we also analyze the performance of two versions of the ensemble Kalman filter (EnKF) in this comparative study. We present numerical results from Monte Carlo simulations to analyze the state estimation accuracy and computational cost of these filters.
机译:在我们以前的工作中,我们使用笛卡尔坐标和修改的球形坐标在3D中进行扩展卡尔曼滤波器(EKF),UNSENTED卡尔曼滤波器(UKF)和粒子过滤器(PF)的粒子滤波器(PF)的性能。 (MSC)对于相对状态向量。我们发现UKF-MSC和EKF-MSC的准确性最佳,UKF-MSC略大于EKF-MSC。与EKF和UKF相比,PF并没有表现良好,计算成本更高。在这项工作中,我们将粒子流量过滤器(PFF)与其他滤波器进行比较了AOF问题的性能。此外,我们还在该比较研究中分析了两种版本的合奏卡尔曼滤波器(ENKF)的性能。我们呈现来自蒙特卡罗模拟的数值结果,以分析这些过滤器的状态估计精度和计算成本。

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