Author
Contributions by role
Author 2
Wei Xiong
Research Institute of Information Fusion, Naval Aviation University, Yantai 264001, China
Summary
Wei Xiong received the Eng.D degree in communication and information system from Naval Aviation Engineering University, China, in 2005.
Edited Journals
IECE Contributions

Open Access | Research Article | 12 April 2025
Dynamic Target Association Algorithm for Unknown Models and Strong Interference
Chinese Journal of Information Fusion | Volume 2, Issue 2: 100-111, 2025 | DOI: 10.62762/CJIF.2025.986522
Abstract
To address the performance degradation of traditional data association algorithms caused by unknown target motion models, environmental interference, and strong maneuvering behaviors in complex dynamic scenarios, this paper proposes an innovative fusion algorithm that integrates reinforcement learning and deep learning. By constructing a policy network that combines Long Short-Term Memory (LSTM) memory units and reinforcement learning dynamic decision-making, a dynamic prediction model for "measurement-target" association probability is established. Additionally, a hybrid predictor incorporating Bayesian networks and multi-order curve fitting is designed to formulate the reward function. To... More >

Graphical Abstract
Dynamic Target Association Algorithm for Unknown Models and Strong Interference

Free Access | Research Article | 29 September 2024 | Cited: 1
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