A Novel Mixture Least Squares Approach for Simultaneous Parameter/State and Unknown Input Estimation

被引：1

作者：

Ding, Bo ^{[1
]}

Wei, Yuanchu ^{[1
]}

Zhang, Yong ^{[2
]}

Yang, Wu ^{[3
]}

机构：

[1] Yangzhou Univ, Coll Informat Engn, Yangzhou 225127, Peoples R China

[2] Wuhan Univ Sci & Technol, Sch Informat Sci & Engn, Wuhan 430081, Peoples R China

[3] Guangxi Univ, Sch Elect Engn, Nanning 530004, Peoples R China

来源：

IEEE TRANSACTIONS ON AUTOMATIC CONTROL | 2025年 / 70卷 / 02期

基金：

中国国家自然科学基金;

关键词：

Covariance matrices; Estimation; Stochastic processes; Maximum likelihood estimation; Kalman filters; Vectors; Filtering algorithms; Linear stochastic systems; mixture least squares (MLSs); parameter/state estimation; unknown input estimation; MINIMUM-VARIANCE ESTIMATION; STATE ESTIMATION; SYSTEMS;

D O I：

10.1109/TAC.2024.3450001

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In this article, we introduce a novel mixture least squares (MLSs) algorithm to deal with the problems of simultaneous parameter/state and unknown input estimation. First, the MLSs algorithm is derived to estimate the desired parameter and unknown input, which can be regarded as a unified framework for deterministic least squares and stochastic least squares. The unbiasedness and optimality of the MLSs estimators are further verified. Then, based on the established MLSs algorithm, a new solution to simultaneous state and unknown input estimation (SUIE) problems is given. The proposed method is more concise and straightforward than the existing SUIE algorithms. The method provided in this article offers fresh insight into parameter/state estimation with unknown input.

引用

页码：1252 / 1258

页数：7

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