Comparison of centralised scaled unscented Kalman filter and extended Kalman filter for multisensor data fusion architectures

Zirui Xing; Yuanqing Xia

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Comparison of centralised scaled unscented Kalman filter and extended Kalman filter for multisensor data fusion architectures

机译：用于多传感器数据融合架构的集中式缩放无味卡尔曼滤波器和扩展卡尔曼滤波器的比较

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This study presents three non-linear centralised scaled unscented Kalman filter (SUKF) for multisensor data fusion algorithms, which are augmented measurements, measurements weighted and sequential filtering fusion. First, the accuracy analysis of extended Kalman filter (EKF) and SUKF is investigated in detail. Second, through comparing the error covariance traces and the absolute mean estimation errors of X and Y directions of centralised SUKF for multisensor data fusion algorithms with that of centralised EKF for multisensor data fusion algorithms, it can be remarked that the performance of centralised augmented measurements SUKF for multisensor data fusion algorithm is the best one among the six algorithms, which is to say that Algorithm (Iu) shows the best performance in accuracy. Finally, combining and synthetically analysing the running time of six algorithms, it illustrates that Algorithm (Iu) is optimal in comprehensive aspects among six algorithms.

机译：这项研究提出了三种用于多传感器数据融合算法的非线性集中式缩放无味卡尔曼滤波器（SUKF），分别是增强测量，加权测量和顺序滤波融合。首先，详细研究了扩展卡尔曼滤波器（EKF）和SUKF的精度分析。其次，通过比较多传感器数据融合算法的集中式SUKF的误差协方差轨迹和X和Y方向的绝对均值估计误差与多传感器数据融合算法的集中式EKF的误差协方差迹线，可以指出集中式增强测量SUKF的性能多传感器数据融合算法是六种算法中最好的一种，也就是说，算法（Iu）在准确性方面表现出最好的性能。最后，综合分析六种算法的运行时间，说明六种算法在综合方面是最优的。

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《Signal Processing, IET》 |2016年第4期|359-365|共7页
作者
Zirui Xing; Yuanqing Xia;
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Beijing Institute of Technology, People's Republic of China;

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Comparison of centralised scaled unscented Kalman filter and extended Kalman filter for multisensor data fusion architectures

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