| UKF算法与SVDKF算法性能的比较 |
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| 引用本文： | 周磊,董乃铭,洪振杰.UKF算法与SVDKF算法性能的比较[J].温州职业技术学院学报,2013(1):81-83,86. |
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| 作者姓名： | 周磊 董乃铭 洪振杰 |
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| 作者单位： | 温州大学数学与信息科学学院 |
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| 摘 要： | 在模式识别领域，基于Unscented的卡尔曼滤波算法（UKF）广受关注，但在求解过程中经常会遇到病态问题，从而影响算法的性能。基于奇异值分解（SVD）的卡尔曼滤波算法（SVDKF）以SVD代替Cholesky分解协方差矩阵产生sigma样本点，可以提高协方差矩阵的数值稳定性。通过对两种算法性能进行仿真比较发现，SVDKF算法优于UKF算法，具有良好的鲁棒性，能有效改善滤波性能，提高算法的精度。
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| 关 键 词： | UKF算法 SVDKF算法 滤波 Cholesky分解 SVD |
Comparison of Algorithm Performances of UKF and SVDKF |
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| Zhou Lei,Dong Naiming,Hong Zhenjie.Comparison of Algorithm Performances of UKF and SVDKF[J].Journal of Wenzhou Vocational & Technical College,2013(1):81-83,86. |
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| Authors: | Zhou Lei Dong Naiming Hong Zhenjie |
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| Institution: | (School of Mathematical and Information Science,Wenzhou University,Wenzhou,325035,China) |
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| Abstract: | In the field of pattern recognition, Kalman filter algorithm （UKF） based on Unscented has been widely used but often encounters the ill-conditioned problems in solving problems, which affects the performance of the algorithm. A Kalman filter algorithm （SVDKF） based on SVD （singular value decomposition） generates sigma samples by using singular value decomposition instead of Cholesky decomposition, and it improves the numerical stability of the covariance matrix. Through simulation experiment on their performances, it is found that SVDKF with higher robustness is better than UKF, and it can effectively improve the performance and accuracy. |
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| Keywords: | UKF SVDKF Filter Cholesky decomposition SVD |
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