论文标题
利用LRKFS中的线性子结构（扩展）
Exploiting Linear Substructure In LRKFs (Extended)
论文作者
论文摘要
我们利用线性回归Kalman过滤器（LRKF）中线性子结构的知识来简化矩匹配的问题。假设部分线性估计模型，理论结果无需估计准确性即可获得可量化和重要的计算加速。结果适用于任何对称的LRKF，计算复杂性的降低是根据立方体规则，估计模型中线性和非线性状态的数量表示的。通过数值示例说明了对过滤问题的影响。
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.
