A Novel Improved Truncated Unscented Kalman Filtering Algorithm

Chao Hou, Liang-qun Li
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Abstract

For the conventional truncated unscented Kalman filtering (TUKF) algorithm requires the measurement to be a bijective function, a novel improved truncated unscented Kalman filtering is proposed. In the proposed algorithm, we linearize the bijective measurements function based on the statistical linear regression (SLR) in order to obtain the only inverse function of the measurement function. It is a modified algorithm which extends the range of practical application of the filtering problems. Finally, the experiments show that the performance of the proposed algorithm is better than the unscented Kalman filter (UKF) and the quadrature Kalman filter (QKF). This approach can efficiently deal with this problem that measurement functions are not bijective.

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一种改进的截断无气味卡尔曼滤波算法
针对传统截断无气味卡尔曼滤波(TUKF)算法要求测量值为双目标函数的特点,提出了一种改进的截断无气味卡尔曼滤波算法。在该算法中,我们基于统计线性回归(SLR)对双射测量函数进行线性化,以获得测量函数的唯一反函数。它是一种改进算法,扩展了滤波问题的实际应用范围。最后,实验表明,该算法的性能优于无气味卡尔曼滤波(UKF)和正交卡尔曼滤波(QKF)。该方法可以有效地解决测量函数不客观的问题。
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