Chinese Journal of Information Fusion

Partner Journal of The Chinese Society of Aeronautics and Astronautics

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  ISSN:  2998-3371 (online)  |  2998-3363 (print)
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Chinese Journal of Information Fusion is a peer-reviewed academic journal reflecting the achievements of cutting-edge research and application of information fusion technology, mainly publishing academic papers in the field of information fusion.
E-mail:[email protected]  DOI Prefix: 10.62762/CJIF
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Recent Articles

Open Access | Research Article | 27 March 2025
A Few-shot Learning Method Using Relation Graph
Chinese Journal of Information Fusion | Volume 2, Issue 1: 70-78, 2025 | DOI:10.62762/CJIF.2025.146072
Abstract
Few-shot learning aims to recognize new-class items under the circumstances with a few labeled support samples. However, many methods may suffer from poor guidance of limited new-class samples that are not suitable for being regarded as class centers. Recent works use word embedding to enrich the new-class distribution message but only use simple mapping between visual and semantic features during training. To solve the aforementioned problems, we propose a method that constructs a class relation graph by semantic meaning as guidance for feature extraction and fusion, to help the learning of the second-order relation information, with a light training request. In addition, we introduce two w... More >

Graphical Abstract
A Few-shot Learning Method Using Relation Graph

Open Access | Research Article | 26 March 2025
Radar Multi-Feature Graph Representation and Graph Network Fusion Target Detection Methods
Chinese Journal of Information Fusion | Volume 2, Issue 1: 59-69, 2025 | DOI:10.62762/CJIF.2025.413277
Abstract
In the context of neural network-based radar feature extraction and detection methods, single-feature detection approaches exhibit limited capability in distinguishing targets from background in complex environments such as sea clutter. To address this, a Multi-Feature Extraction Network and Graph Fusion Detection Network (MFEn-GFDn) method is proposed, leveraging feature complementarity and enhanced information utilization. MFEn extracts features from various time-frequency maps of radar signals to construct Multi-Feature Graph Data (MFG) for multi-feature graphical representation. Subsequently, GFDn performs fusion detection on MFG containing multi-feature information. By expanding the fea... More >

Graphical Abstract
Radar Multi-Feature Graph Representation and Graph Network Fusion Target Detection Methods

Open Access | Research Article | 22 March 2025
A Deep-Learning Detector via Optimized YOLOv7-bw Architecture for Dense Small Remote-Sensing Targets in Harsh Food Supply Applications
Chinese Journal of Information Fusion | Volume 2, Issue 1: 38-58, 2025 | DOI:10.62762/CJIF.2025.919344
Abstract
With the progressive advancement of remote sensing image technology, its application in the agricultural domain is becoming increasingly prevalent. Both cultivation and transportation processes can greatly benefit from utilizing remote sensing images to ensure adequate food supply. However, such images often exist in harsh environments with many gaps and dense distribution, which poses major challenges to traditional target detection methods. The frequent missed detections and inaccurate bounding boxes severely constrain the further analysis and application of remote sensing images within the agricultural sector. This study presents an enhanced version of the YOLO algorithm, specifically tai... More >

Graphical Abstract
A Deep-Learning Detector via Optimized YOLOv7-bw Architecture for Dense Small Remote-Sensing Targets in Harsh Food Supply Applications

Open Access | Research Article | 20 March 2025
Integrating Relationship Path and Entity Neighbourhood Information for Knowledge Graph Intelligence of Social Things
Chinese Journal of Information Fusion | Volume 2, Issue 1: 27-37, 2025 | DOI:10.62762/CJIF.2025.197460
Abstract
In the evolving framework of the Intelligence of Social Things (IoST), which amalgamates social networks and IoT ecosystems, knowledge graphs are essential for facilitating networked systems to efficiently process and leverage intricate relational data. Knowledge graphs offer essential technical assistance for various artificial intelligence applications, such as e-commerce, intelligent navigation, healthcare, and social media. Nonetheless, current knowledge graphs frequently lack completeness, harboring a considerable quantity of implicit knowledge that remains to be revealed. Consequently, tackling the difficulty of finalising knowledge graphs has emerged as a pressing research priority. M... More >

Graphical Abstract
Integrating Relationship Path and Entity Neighbourhood Information for Knowledge Graph Intelligence of Social Things

Open Access | Research Article | 17 March 2025
Quantitative Evaluation Method for Anomaly Levels of Complex Flight Maneuver Based on Multi-sensor Data
Chinese Journal of Information Fusion | Volume 2, Issue 1: 14-26, 2025 | DOI:10.62762/CJIF.2024.344084
Abstract
The methods that identify complex flight maneuvers from multi-sensor flight parameter data and conduct automated quantitative evaluations of anomaly levels could play an important role in enhancing flight safety and pilot training. However, existing methods focus on anomaly detection at individual flight parameter data points, making it challenging to accurately quantify the overall abnormality of a flight maneuver. To address this issue, this paper proposes a novel method for the quantitative evaluation of anomaly levels in complex flight maneuvers by fusing multi-sensor data. The proposed method comprises two stages: complex flight maneuver recognition and anomaly level quantification. In... More >

Graphical Abstract
Quantitative Evaluation Method for Anomaly Levels of Complex Flight Maneuver Based on Multi-sensor Data

Open Access | Research Article | 23 January 2025
Intelligent System Architecture Based on System Theory
Chinese Journal of Information Fusion | Volume 2, Issue 1: 1-13, 2025 | DOI:10.62762/CJIF.2024.872211
Abstract
Intelligent system is a research field that attracts much attention at present. Most of the researches on intelligent system focus on intelligent technology and its application. However, an intelligent system is first of all a system, which means it should have the characteristics of a system. Design of conventional system is mainly function- or task-oriented, and adaptation to environment is passive, static and regular. However, intelligent system is faced with a complex, random and dynamic environment, and has dynamic interaction with the environment. Behind this interaction behavior is a fusion of thinking and learning processes, and behind thinking learning is mission task and value. The... More >

Graphical Abstract
Intelligent System Architecture Based on System Theory

Code (Data) Available | Open Access | Research Article | 31 December 2024
DMFuse: Diffusion Model Guided Cross-Attention Learning for Infrared and Visible Image Fusion
Chinese Journal of Information Fusion | Volume 1, Issue 3: 226-242, 2024 | DOI:10.62762/CJIF.2024.655617
Abstract
Image fusion aims to integrate complementary information from different sensors into a single fused output for superior visual description and scene understanding. The existing GAN-based fusion methods generally suffer from multiple challenges, such as unexplainable mechanism, unstable training, and mode collapse, which may affect the fusion quality. To overcome these limitations, this paper introduces a diffusion model guided cross-attention learning network, termed as DMFuse, for infrared and visible image fusion. Firstly, to improve the diffusion inference efficiency, we compress the quadruple channels of the denoising UNet network to achieve more efficient and robust model for fusion tas... More >

Graphical Abstract
DMFuse: Diffusion Model Guided Cross-Attention Learning for Infrared and Visible Image Fusion

Free Access | Research Article | 30 December 2024
Robust Distributed State Estimation in Power Systems: A Multi-Estimator Data Fusion Approach to Counteract Cyber-Attacks
Chinese Journal of Information Fusion | Volume 1, Issue 3: 212-225, 2024 | DOI:10.62762/CJIF.2024.740709
Abstract
Cyber security in power systems has become increasingly critical with the rise of network attacks such as Denial-of-Service (DoS) attacks and False Data Injection (FDI) attacks. These threats can severely compromise the integrity and reliability of state estimation, which are fundamental to the operation and control of power systems. In this manuscript, an estimation algorithm based on the fusion of information from multiple estimators is proposed to ensure that state estimation at critical buses can function properly in case of attacks. Our approach leverages a network of estimators that can dynamically adjust to maintain system stability and accuracy. Furthermore, a new detector is adopted... More >

Graphical Abstract
Robust Distributed State Estimation in Power Systems: A Multi-Estimator Data Fusion Approach to Counteract Cyber-Attacks

Code (Data) Available | Free Access | Review Article | 15 December 2024 | Cited: 1
A Comprehensive Survey on Emerging Techniques and Technologies in Spatio-Temporal EEG Data Analysis
Chinese Journal of Information Fusion | Volume 1, Issue 3: 183-211, 2024 | DOI:10.62762/CJIF.2024.876830
Abstract
In recent years, the field of electroencephalography (EEG) analysis has witnessed remarkable advancements, driven by the integration of machine learning and artificial intelligence. This survey aims to encapsulate the latest developments, focusing on emerging methods and technologies that are poised to transform our comprehension and interpretation of brain activity. The structure of this paper is organized according to the categorization within the machine learning community, with representation learning as the foundational concept that encompasses both discriminative and generative approaches. We delve into self-supervised learning methods that enable the robust representation of brain sig... More >

Graphical Abstract
A Comprehensive Survey on Emerging Techniques and Technologies in Spatio-Temporal EEG Data Analysis

Free Access | Research Article | 07 December 2024
Basic Belief Assignment Determination Based on Radial Basis Function Network
Chinese Journal of Information Fusion | Volume 1, Issue 3: 175-182, 2024 | DOI:10.62762/CJIF.2024.841250
Abstract
In Dempster-Shafer evidence theory (DST), the determination of basic belief assignment (BBA) is an important yet challenging issue. The rational mass determination of compound focal elements is crucial for fully taking advantage of DST, i.e., the ability to represent the ambiguity. In this paper, for the compound focal elements, we select and construct the \enquote{compound-class samples} with ambiguous class membership. Then, we use these samples to construct an end-to-end model called Evidential Radial Basis Function Network (E-RBFN), with the input as the sample and the output as the corresponding BBA. The E-RBFN can directly determine the mass values for all focal elements (including the... More >

Graphical Abstract
Basic Belief Assignment Determination Based on Radial Basis Function Network

Free Access | Research Article | 30 September 2024 | Cited: 2
Unsupervised Industrial Anomaly Detection Based on Feature Mask Generation and Reverse Distillation
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 | Cited: 8
Complex Evidence Theory for Multisource Data Fusion
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
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 | Cited: 1
A High-Efficiency Two-Layer Path Planning Method for UAVs in Vast Airspace
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 | Cited: 1
Convolutional Neural Network for Ellipse Extended Target Tracking
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

Free Access | Review Article | 12 June 2024 | Cited: 3
Bridging Modalities: A Survey of Cross-Modal Image-Text Retrieval
Chinese Journal of Information Fusion | Volume 1, Issue 1: 79-92, 2024 | DOI:10.62762/CJIF.2024.361895
Abstract
The rapid advancement of Internet technology, driven by social media and e-commerce platforms, has facilitated the generation and sharing of multimodal data, leading to increased interest in efficient cross-modal retrieval systems. Cross-modal image-text retrieval, encompassing tasks such as image query text (IqT) retrieval and text query image (TqI) retrieval, plays a crucial role in semantic searches across modalities. This paper presents a comprehensive survey of cross-modal image-text retrieval, addressing the limitations of previous studies that focused on single perspectives such as subspace learning or deep learning models. We categorize existing models into single-tower, dual-tower,... More >

Graphical Abstract
Bridging Modalities: A Survey of Cross-Modal Image-Text Retrieval

Free Access | Research Article | Feature Paper | 10 June 2024 | Cited: 2
Extraction of Motion Information from Occupancy Grid Map Using Keystone Transform
Chinese Journal of Information Fusion | Volume 1, Issue 1: 63-78, 2024 | DOI:10.62762/CJIF.2024.361892
Abstract
Considering the tractability of OGM (Occupancy Grid Map) and its wide use in the dynamic environment representation of mobile robotics, the extraction of motion information from successive OGMs are very important for many tasks, such as SLAM (Simultaneously Localization And Mapping) and DATMO (Detection and Tracking of Moving Object). In this paper, we propose a novel motion extraction method based on the signal transform, called as S-KST (Spatial Keystone Transform), for the motion detection and estimation from successive noisy OGMs. It extends the KST in radar imaging or motion compensation to 1D spatial case (1DS-KST) and 2D spatial case (2DS-KST) combined multiple hypotheses about poss... More >

Graphical Abstract
Extraction of Motion Information from Occupancy Grid Map Using Keystone Transform

Free Access | Research Article | 08 June 2024 | Cited: 3
GPS Tracking Based on Stacked-Serial LSTM Network
Chinese Journal of Information Fusion | Volume 1, Issue 1: 50-62, 2024 | DOI:10.62762/CJIF.2024.361889
Abstract
Maneuvering target tracking is widely used in unmanned vehicles, missile navigation, underwater ships, etc. Due to the uncertainty of the moving characteristics of maneuvering targets and the low sensor measurement accuracy, trajectory tracking has always been an open research problem and challenging work. This paper proposes a trajectory estimation method based on LSTM neural network for uncertain motion characteristics. The network consists of two LSTM networks with stacked serial relationships, one of which is used to predict the movement dynamics, and the other is used to update the track's state. Compared with the classical Kalman filter based on the maneuver model, the method proposed... More >

Graphical Abstract
GPS Tracking Based on Stacked-Serial LSTM Network

Author's Talk | Free Access | Research Article | Feature Paper | 28 May 2024 | Cited: 9
A Mimic Fusion Algorithm for Dual Channel Video Based on Possibility Distribution Synthesis Theory
Chinese Journal of Information Fusion | Volume 1, Issue 1: 33-49, 2024 | DOI:10.62762/CJIF.2024.361886
Abstract
In response to the current practical fusion requirements for infrared and visible videos, which often involve collaborative fusion of difference feature information, and model cannot dynamically adjust the fusion strategy according to the difference between videos, resulting in poor fusion performance, a mimic fusion algorithm for infrared and visible videos based on the possibility distribution synthesis theory is proposed. Firstly, quantitatively describe the various difference features and their attributes of the region of interest in each frame of the dual channel video sequence, and select the main difference features corresponding to each frame. Secondly, the pearson correlation coeffi... More >

Graphical Abstract
Author's Talk
A Mimic Fusion Algorithm for Dual Channel Video Based on Possibility Distribution Synthesis Theory
A Mimic Fusion Algorithm for Dual Channel Video Based on Possibility Distribution Synthesis Theory

Free Access | Research Article | Feature Paper | 27 May 2024 | Cited: 7
Simultaneous Spatiotemporal Bias Compensation and Data Fusion for Asynchronous Multisensor Systems
Chinese Journal of Information Fusion | Volume 1, Issue 1: 16-32, 2024 | DOI:10.62762/CJIF.2024.361881
Abstract
Bias estimation of sensors is an essential prerequisite for accurate data fusion. Neglect of temporal bias in general real systems prevents the existing algorithms from successful application. In this paper, both spatial and temporal biases in asynchronous multisensor systems are investigated and two novel methods for simultaneous spatiotemporal bias compensation and data fusion are presented. The general situation that the sensors sample at different times with different and varying periods is explored, and unknown time delays may exist between the time stamps and the true measurement times. Due to the time delays, the time stamp interval of the measurements from different sensors may be di... More >

Graphical Abstract
Simultaneous Spatiotemporal Bias Compensation and Data Fusion for Asynchronous Multisensor Systems

Free Access | Research Article | 25 May 2024 | Cited: 5
Research on A Ship Trajectory Classification Method Based on Deep Learning
Chinese Journal of Information Fusion | Volume 1, Issue 1: 3-15, 2024 | DOI:10.62762/CJIF.2024.361873
Abstract
The unrestricted development and utilization of marine resources have resulted in a series of practical problems, such as the destruction of marine ecology. The wide application of radar, satellites and other detection equipment has gradually led to a large variety of large-capacity marine spatiotemporal trajectory data from a vast number of sources. In the field of marine domain awareness, there is an urgent need to use relevant information technology means to control and monitor ships and accurately classify and identify ship behavior patterns through multisource data fusion analysis. In addition, the increase in the type and quantity of trajectory data has produced a corresponding increa... More >

Graphical Abstract
Research on A Ship Trajectory Classification Method Based on Deep Learning

Open Access | Editorial | 23 May 2024
Inaugural Editorial of the Chinese Journal of Information Fusion
Chinese Journal of Information Fusion | Volume 1, Issue 1: 1-2, 2024 | DOI:10.62762/CJIF.2024.100001
Abstract
Presents the introductory editorial for this issue of the publication. More >
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Chinese Journal of Information Fusion

Chinese Journal of Information Fusion

eISSN: 2998-3371 | pISSN: 2998-3363

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