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Exploiting Linear Substructure In LRKFs (Extended)
arXiv - CS - Systems and Control Pub Date : 2020-09-16 , DOI: arxiv-2009.07571
M. Greiff, K. Berntorp and A. Robertsson

We exploit knowledge of linear substructure in the linear-regression Kalman filters (LRKFs) to simplify the problem of moment matching. The theoretical results yield quantifiable and significant computational speedups at no cost of estimation accuracy, assuming partially linear estimation models. The results apply to any symmetrical LRKF, and reductions in computational complexity are stated as a function of the cubature rule, the number of linear and nonlinear states in the estimation model respectively. The implications for the filtering problem are illustrated by numerical examples.

中文翻译:

在 LRKF 中利用线性子结构(扩展)

我们利用线性回归卡尔曼滤波器 (LRKF) 中线性子结构的知识来简化矩匹配问题。假设部分线性估计模型,理论结果在不损失估计精度的情况下产生了可量化和显着的计算加速。结果适用于任何对称的 LRKF,并且计算复杂度的降低被表述为体积规则、估计模型中线性和非线性状态的数量的函数。数值例子说明了对滤波问题的影响。
更新日期:2020-09-17
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