Sensor Fusion Methods for State Estimation in Cyber-Physical Systems

Combining measurements from several sensors can give more accurate and reliable estimates than any single sensor. This review presents the main fusion levels and estimation methods, from the Kalman filter to its nonlinear extensions, and their typical limits. No new experimental data are reported. An AI language model (Claude, Anthropic) was used in preparing this manuscript.

Authors

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-11-03
DOI
https://doi.org/10.5281/zenodo.23917657
Primary Topic
Target Tracking and Data Fusion in Sensor Networks
Type
article
Field-Weighted Citation Impact
0.00
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article

Sensor Fusion Methods for State Estimation in Cyber-Physical Systems

Yslam Orazov, Ayjemal Ussayeva
Zenodo (CERN European Organization for Nuclear Research)
Target Tracking and Data Fusion in Sensor Networks
article

Sensor Fusion Methods for State Estimation in Cyber-Physical Systems

Yslam Orazov, Ayjemal Ussayeva
article en

Abstract

Combining measurements from several sensors can give more accurate and reliable estimates than any single sensor. This review presents the main fusion levels and estimation methods, from the Kalman filter to its nonlinear extensions, and their typical limits. No new experimental data are reported. An AI language model (Claude, Anthropic) was used in preparing this manuscript.

Zenodo (CERN European Organization for Nuclear Research)
Openalex Percentile: Top 16%
Target Tracking and Data Fusion in Sensor Networks
DOIOpen Access PDFOpenAlex
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Sensor Fusion Methods for State Estimation in Cyber-Physical Systems — Yslam Orazov, Ayjemal Ussayeva · Zenodo (CERN European Organization for Nuclear Research) (2026) | TGRS Research Map | TGRS