论文标题
线性最小二估计的教程
A Tutorial on Linear Least Square Estimation
论文作者
论文摘要
这是关于最小平方估计技术的简短教程，该技术直接而有效，可用于参数估计。该教程集中在线性LSES而不是非线性版本上，因为大多数非线性LSE可以使用其线性对应物进行非平整性近似。线性LSE还可以深入了解非线性技术的研究，例如Gauss-Newton方法和Lavenberg-Marquardt方法等。线性LSE在大多数情况下在计算上是有效的，因此它们在实践中被广泛应用。在本教程中，对原始批次最小成方的估计及其递归变体都通过详细的数学推导进行了全面审查。
This is a brief tutorial on the least square estimation technique that is straightforward yet effective for parameter estimation. The tutorial is focused on the linear LSEs instead of nonlinear versions, since most nonlinear LSEs can be approximated non-trivially using its linear counterparts. Linear LSEs can also provide insight into the study of the nonlinear techniques, e.g., Gauss-Newton method and Lavenberg-Marquardt method etc. Linear LSEs are computationally efficient for most occasions, so they are widely applied in practice. In this tutorial, both the original batch least square estimation and its recursive variants are reviewed comprehensively with detailed mathematical derivations.
