2022/11/28 by Qingrui Zhang, Zhang, Qingrui · 1 citation
Computer Science · Engineering · #Control Systems and Identification #FOS: Electrical engineering #Neural Networks and Applications #Sensor Technology and Measurement Systems #Systems and Control (eess.SY) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2211.15347
openalex publication_date 2022/11/28 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.