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Electronics
Special Issues
Data Analysis and Data Fusion in System Identification and Measurements
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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: closed (15 December 2025) | Viewed by 3031

Special Issue Editors

Dr. Guanghua Zhang

E-Mail Website
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
Prof. Dr. Hui Chen

E-Mail Website
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
Dr. Yulan Han

E-Mail Website
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

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

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

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Published Papers (4 papers)

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Research

25 pages, 4395 KB  
Article
Correlation-Aware Multimodal Fusion Network for Fashion Compatibility Modeling
by Yan Fang, Jiangnan Ge, Ran Xiao and Yidan Zhang
Electronics 2026, 15(2), 332; https://doi.org/10.3390/electronics15020332 - 12 Jan 2026
Viewed by 298
Abstract
The rapid growth of e-commerce and the booming online fashion industry are driving growing user demand for sophisticated, compatible fashion outfits. As an emerging multimodal information retrieval technology, fashion compatibility modeling aims to predict the compatibility degree for any given outfit and provide [...] Read more.
The rapid growth of e-commerce and the booming online fashion industry are driving growing user demand for sophisticated, compatible fashion outfits. As an emerging multimodal information retrieval technology, fashion compatibility modeling aims to predict the compatibility degree for any given outfit and provide complementary item recommendations for incomplete outfits. Although existing research has made significant progress in exploring fashion compatibility tasks from a multimodal perspective, it has yet to fully exploit the multimodal information and correlations among fashion items. To effectively tackle these challenges, a correlation-aware multimodal fusion network for fashion compatibility modeling is proposed. Long-distance correlated visual features are investigated during multimodal processing to enhance the quality of visual features. An improved dual-interaction mechanism is used to achieve deep multimodal fusion. Furthermore, we explore both negative and multi-scale correlations to obtain complex correlations among items and thereby enhance the accuracy of fashion compatibility assessment. Extensive experiments on real-world fashion datasets demonstrate that our method outperforms existing advanced benchmark models in AUC and ACC metrics. This indicates the efficiency of our model in enhancing fashion compatibility evaluation performance. Full article
(This article belongs to the Special Issue Data Analysis and Data Fusion in System Identification and Measurements)
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30 pages, 13588 KB  
Article
MSTFT: Mamba-Based Spatio-Temporal Fusion for Small Object Tracking in UAV Videos
by Kang Sun, Haoyang Zhang and Hui Chen
Electronics 2026, 15(2), 256; https://doi.org/10.3390/electronics15020256 - 6 Jan 2026
Viewed by 351
Abstract
Unmanned Aerial Vehicle (UAV) visual tracking is widely used but continues to face challenges such as unpredictable target motion, error accumulation, and the sparse appearance of small targets. To address these issues, we propose a Mamba-based Spatio-Temporal Fusion Tracker. To address tracking drift [...] Read more.
Unmanned Aerial Vehicle (UAV) visual tracking is widely used but continues to face challenges such as unpredictable target motion, error accumulation, and the sparse appearance of small targets. To address these issues, we propose a Mamba-based Spatio-Temporal Fusion Tracker. To address tracking drift from large displacements and abrupt pose changes, we first introduce a Bidirectional Spatio-Temporal Mamba module. It employs bidirectional spatial scanning to capture discriminative local features and temporal scanning to model dynamic motion patterns. Second, to suppress error accumulation in complex scenes, we develop a Dynamic Template Fusion module with Adaptive Attention. This module integrates a threefold safety verification mechanism—based on response peak, temporal consistency, and motion stability—with a scale-aware strategy to enable robust template updates. Moreover, we design a Small-Target-Aware Context Prediction Head that utilizes a Gaussian-weighted prior to guide feature fusion and refines the loss function, significantly improving localization accuracy under sparse target features and strong background interference. On three major UAV tracking benchmarks (UAV123, UAV123@10fps, and UAV20L), our MSTFT establishes new state-of-the-art with success AUCs of 79.4%, 76.5%, and 75.8% respectively. More importantly, it maintains a tracking speed of 45 FPS, demonstrating a superior balance between precision and efficiency. Full article
(This article belongs to the Special Issue Data Analysis and Data Fusion in System Identification and Measurements)
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26 pages, 952 KB  
Article
From Forecasting to Foresight: Building an Autonomous O&M Brain for the New Power System Based on a Cognitive Digital Twin
by Xufeng Wu, Zuowei Chen, Hefang Jiang, Shoukang Luo, Yi Zhao, Dongwei Zhao, Peiyao Dang, Jiajun Gao, Lin Lin and Hao Wang
Electronics 2025, 14(22), 4537; https://doi.org/10.3390/electronics14224537 - 20 Nov 2025
Cited by 2 | Viewed by 815
Abstract
Despite notable advances in load forecasting and fault detection, current power system operation and maintenance (O&M) technologies remain fragmented into independent and primarily reactive modules. Load forecasting estimates future demand, whereas fault detection identifies whether abnormal conditions exist in the present state. This [...] Read more.
Despite notable advances in load forecasting and fault detection, current power system operation and maintenance (O&M) technologies remain fragmented into independent and primarily reactive modules. Load forecasting estimates future demand, whereas fault detection identifies whether abnormal conditions exist in the present state. This paper proposes a unified and proactive Cognitive Digital Twin (CDT) system. Unlike traditional data-driven approaches, the CDT integrates Large Language Models (LLMs) and Knowledge Graphs (KGs) as cognitive cores to enable deeper reasoning and context-aware decision-making. The CDT system not only mirrors the physical grid but also acts as an intelligent O&M engine capable of understanding, reasoning, predicting, and self-diagnosing. The core innovation lies in prediction-based anomaly detection. The system first estimates the expected healthy state of the grid at future time steps and then compares real-time monitoring data against these predictions to identify incipient anomalies. This enables genuine foresight rather than simple reactive detection. By orchestrating advanced analytical modules, including CNN–LSTM hybrid models and optimization algorithms, the CDT supports autonomous O&M operations with transparent and explainable decision-making. These capabilities enhance grid resilience and improve the system’s capacity for self-healing. Full article
(This article belongs to the Special Issue Data Analysis and Data Fusion in System Identification and Measurements)
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15 pages, 5470 KB  
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
Cited by 2 | Viewed by 1051
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
(This article belongs to the Special Issue Data Analysis and Data Fusion in System Identification and Measurements)
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