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Computationally Efficient Multisensor Fusion Estimation Algorithms

机译:计算有效的多传感器融合估计算法

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This paper provides two computationally effective fusion estimation algorithms. The first algorithm is based on Cholesky factorization of a cross-covariance block matrix. This algorithm has low computational complexity and is equivalent to the standard composite fusion estimation algorithm as well. The second algorithm is based on a special approximation scheme for local cross-covariances. Such approximation is useful to compute matrix weights for fusion estimation in a multidimensional-multisensor environment. Subsequent computational analysis of the proposed fusion algorithms is presented with corresponding examples showing the low computational complexities of the new fusion estimation algorithms. ©2010 American Society of Mechanical Engineers
机译:本文提供了两种计算有效的融合估计算法。第一种算法基于互协方差块矩阵的Cholesky分解。该算法具有较低的计算复杂度,并且也等同于标准复合融合估计算法。第二种算法基于局部互协方差的特殊近似方案。这种近似对于在多维多传感器环境中计算矩阵权重进行融合估计很有用。给出了所提出的融合算法的后续计算分析,并给出了相应的示例,这些示例说明了新融合估计算法的低计算复杂性。 ©2010美国机械工程师学会

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