Data Analysis and Data Fusion in System Identification and Measurements

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Systems & Control Engineering".

Deadline for manuscript submissions: 15 June 2025 | Viewed by 610

Special Issue Editors


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Guest Editor
Ministry of Education Key Laboratory for Intelligent Networks and Network Security, School of Automation Science and Engineering, Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi’an 710049, China
Interests: Multi-source information fusion; estimation and filtering; target tracking

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Guest Editor
Department of Automation, School of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou 730050, China
Interests: data fusion; multi-target tracking; sensor management; estimation and filtering

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Guest Editor
School of Electronic and Electrical Engineering, Faculty of Mathematics Physics and Information Sciences, Ningxia University, Yinchuan 750021, China
Interests: Target tracking; Information fusion; Intelligent control

Special Issue Information

Dear Colleagues,

With the advent of the big data era, data has become an indispensable resource in modern society. In the field of system identification and measurement, how to effectively analyze and fuse data from different sources, formats, and qualities to improve the accuracy of system identification and measurement precision has become a current research hotspot. Data analysis is a core skill in the big data era. In system identification and measurement, data analysis techniques enable us to extract valuable information from massive datasets and reveal the inherent patterns and correlations within the data. Data fusion is a technology that integrates, optimizes, and utilizes data from different sources. In system identification and measurement, data fusion techniques can significantly enhance data reliability and accuracy, thereby improving the precision of system identification and measurement.  

This Special Issue focuses on the theme of "Data Analysis and Data Fusion in System Identification and Measurements", aiming to explore the applications and advancements of data analysis and data fusion technologies in the field of system identification and measurement. Prospective authors are invited to submit their novel and original manuscripts on the theoretical underpinnings and the practical applications of these techniques. Potential topics of interest include, but are not limited to, the following: 

  • Multi-source information fusion;
  • Bayesian estimation theory;
  • Advanced signal and information processing;
  • Target detection, recognition, and tracking;
  • Cooperative localization and tracking;
  • Sensor fusion in navigation systems;
  • System identification;
  • Simultaneous localization and target tracking;
  • Networked estimation and filtering.

Dr. Guanghua Zhang
Prof. Dr. Hui Chen
Dr. Yulan Han
Guest Editors

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Keywords

  • data analysis
  • data fusion
  • system identification
  • signal processing
  • estimation and filtering
  • target detection, recognition, and tracking

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Published Papers (1 paper)

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Research

15 pages, 5470 KiB  
Communication
Multi-Source Spatio-Temporal Data Fusion Path Estimation Method
by Qinying Hu, Gege Sun and Hang Chen
Electronics 2025, 14(4), 788; https://doi.org/10.3390/electronics14040788 - 18 Feb 2025
Viewed by 388
Abstract
To address the problem of overlooking target movement characteristics and historical activity patterns in conventional path estimation methods, we propose a method based on the principle of multi-source spatio-temporal data fusion. It integrates optical image data with navigation and positioning data and improves [...] Read more.
To address the problem of overlooking target movement characteristics and historical activity patterns in conventional path estimation methods, we propose a method based on the principle of multi-source spatio-temporal data fusion. It integrates optical image data with navigation and positioning data and improves the A* algorithm. While seeking the shortest path, the algorithm prioritizes points within hotspot areas to achieve accurate target path estimation. The algorithm extracts hotspot areas using spatial analysis methods such as kernel density analysis and uses them as the basis for path estimation. Through many simulation experiments, it is verified that the proposed improved the A* algorithm is more consistent with the actual path than the traditional A* algorithm. Full article
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