Data-driven end-to-end state estimation algorithm based on subspace identification

IF 3 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Digital Signal Processing Pub Date : 2025-09-01 DOI:10.1016/j.dsp.2025.105554

Yajing Cheng , Gang Hao

引用次数: 0

Abstract

A data-driven end-to-end state estimation algorithm for multi-input multi-output (MI-MO) high-dimensional linear systems is proposed in this paper. The proposed algorithm does not rely on any prior knowledge and instead utilizes measured input/output (I/O) data for state estimation. This algorithm is based on subspace identification technology and can handle state estimation of black box systems. The proposed algorithm consists of batch state estimation algorithm based on subspace (SI_BSE) and recursive state estimation algorithm based on subspace identification (SI_RSE). The efficacy of the proposed algorithms are verified through simulation.

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基于子空间识别的数据驱动端到端状态估计算法

针对多输入多输出（MI-MO）高维线性系统，提出了一种数据驱动的端到端状态估计算法。该算法不依赖于任何先验知识，而是利用测量的输入/输出（I/O）数据进行状态估计。该算法基于子空间识别技术，能够处理黑匣子系统的状态估计问题。该算法包括基于子空间的批处理状态估计算法（SI_BSE）和基于子空间识别的递归状态估计算法（SI_RSE）。通过仿真验证了所提算法的有效性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Digital Signal Processing 工程技术-工程：电子与电气

CiteScore

5.30

自引率

17.20%

发文量

435

审稿时长

66 days

期刊介绍： Digital Signal Processing: A Review Journal is one of the oldest and most established journals in the field of signal processing yet it aims to be the most innovative. The Journal invites top quality research articles at the frontiers of research in all aspects of signal processing. Our objective is to provide a platform for the publication of ground-breaking research in signal processing with both academic and industrial appeal. The journal has a special emphasis on statistical signal processing methodology such as Bayesian signal processing, and encourages articles on emerging applications of signal processing such as: • big data• machine learning• internet of things• information security• systems biology and computational biology,• financial time series analysis,• autonomous vehicles,• quantum computing,• neuromorphic engineering,• human-computer interaction and intelligent user interfaces,• environmental signal processing,• geophysical signal processing including seismic signal processing,• chemioinformatics and bioinformatics,• audio, visual and performance arts,• disaster management and prevention,• renewable energy,