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    Volume 1, Issue 2 - Table of Contents
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    Cover Story: This article provides a comprehensive review of data fusion methods based on Dempster–Shafer evidence theory and its extensions, collectively referred to as classical evidence theory, examining three aspects: uncertainty modeling, fusion, and decision making. The study explores complex evidence theory for data fusion in both closed and open world contexts, utilizing the framework of complex plane modeling. Additionally, the article presents multisource data fusion algorithms based on classical and complex evidence theory frameworks, applied to pattern classification to compare and demonstrate their applicability. The research results indicate that the complex evidence theory framework can enhance the capabilities of uncertainty modeling and reasoning by generating constructive interference through the fusion of appropriate complex basic belief assignment functions modeled by complex numbers.
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    Free Access | Research Article | 30 September 2024
    Unsupervised Industrial Anomaly Detection Based on Feature Mask Generation and Reverse Distillation
    by
    Pei Qi
    ,
    Lin Chai
    ,
    Xinyu Ye
    Chinese Journal of Information Fusion | Volume 1, Issue 2: 160-174, 2024 | DOI:10.62762/CJIF.2024.734267
    Abstract
    In the realm of industrial defect detection, unsupervised anomaly detection methods draw considerable attention as a result of their exceptional accomplishments. Among these, knowledge distillation-based methods have emerged as a prominent research focus, favored for their streamlined architecture, precision, and efficiency. However, the challenge of characterizing the variability in anomaly samples hinders the accuracy of detection. To address this issue, our research presents a novel approach for anomaly detection and localization, leveraging the concept of inverse knowledge distillation as its cornerstone. We employ the encoder as the guiding teacher model and designate the decoder as the... More >
    Graphical Abstract
    Unsupervised Industrial Anomaly Detection Based on Feature Mask Generation and Reverse Distillation
    Free Access | Review Article | Feature Paper | 30 September 2024
    Complex Evidence Theory for Multisource Data Fusion
    by
    Fuyuan Xiao
    ,
    Junhao Wen
    ,
    Witold Pedrycz
    ,
    Masayoshi Aritsugi
    Chinese Journal of Information Fusion | Volume 1, Issue 2: 134-159, 2024 | DOI:10.62762/CJIF.2024.999646
    Abstract
    Data fusion is a prevalent technique for assembling imperfect raw data coming from multiple sources to capture reliable and accurate information. Dempster–Shafer evidence theory is one of useful methodologies in the fusion of uncertain multisource information. The existing literature lacks a thorough and comprehensive review of the recent advances of Dempster– Shafer evidence theory for data fusion. Therefore, the state of the art has to be surveyed to gain insight into how Dempster–Shafer evidence theory is beneficial for data fusion and how it evolved over time. In this paper, we first provide a comprehensive review of data fusion methods based on Dempster–Shafer evidence theory an... More >
    Graphical Abstract
    Complex Evidence Theory for Multisource Data Fusion
    Free Access | Research Article | 29 September 2024
    Explainable Classification of Remote Sensing Ship Images Based on Graph Network
    by
    Haoran Li
    ,
    Wei Xiong
    ,
    Yaqi Cui
    ,
    Zhenyu Xiong
    Chinese Journal of Information Fusion | Volume 1, Issue 2: 126-133, 2024 | DOI:10.62762/CJIF.2024.932552
    Abstract
    Remote sensing image plays an important role in maritime surveillance, and as a result there is increasingly becoming a prominent focus on the detection and recognition of maritime objects. However, most existing studies in remote sensing image classification pay more attention on the performance of model, thus neglecting the transparency and explainability in it. To address the issue, an explainable classification method based on graph network is proposed in the present study, which seeks to make use of the relationship between objects' regions to infer the category information. First, the local visual attention module is designed to focus on different but important regions of the object. T... More >
    Graphical Abstract
    Explainable Classification of Remote Sensing Ship Images Based on Graph Network
    Free Access | Research Article | 27 September 2024
    A High-Efficiency Two-Layer Path Planning Method for UAVs in Vast Airspace
    by
    Tongyao Yang
    ,
    Fengbao Yang
    Chinese Journal of Information Fusion | Volume 1, Issue 2: 109-125, 2024 | DOI:10.62762/CJIF.2024.596648
    Abstract
    In response to the challenges associated with the inefficiency and poor quality of 3D path planning for Unmanned Aerial Systems (UAS) operating in vast airspace, a novel two-layer path planning method is proposed based on a divide-and-conquer methodology. This method segregates the solution process into two distinct stages: heading planning and path planning, thereby ensuring the planning of both efficiency and path quality. Firstly, the path planning phase is formulated as a multi-objective optimization problem, taking into account the environmental constraints of the UAV mission and path safety. Subsequently, the multi-dimensional environmental data is transformed into a two-dimensional pr... More >
    Graphical Abstract
    A High-Efficiency Two-Layer Path Planning Method for UAVs in Vast Airspace
    Free Access | Research Article | 23 September 2024
    Convolutional Neural Network for Ellipse Extended Target Tracking
    by
    Binchao Bian
    ,
    Hui Chen
    Chinese Journal of Information Fusion | Volume 1, Issue 2: 93-108, 2024 | DOI:10.62762/CJIF.2024.160538
    Abstract
    In the field of extended target tracking, constrained by the sparse measurement set from radar, the target contour is commonly estimated as an elliptical shape. This paper uses convolutional neural networks to estimate the size and orientation information of extended targets. First, by establishing a systematic model for elliptical extended targets and modeling its measurement information, data normalization, and length equalization operations were conducted to provide reliable measurement data for subsequent neural network processing. Subsequently, through the construction of a convolutional neural network model, accurate estimation of the contour parameters of elliptical extended targets w... More >
    Graphical Abstract
    Convolutional Neural Network for Ellipse Extended Target Tracking