TY  - JOUR
AU  - Su, Ningyuan
AU  - Chen, Xiaolong
AU  - Guan, Jian
AU  - Wang, Xinghai
AU  - Zhou, Liangjiang
AU  - Wang, Jinhao
AU  - Wang, Hongyong
PY  - 2025
DA  - 2025/03/26
TI  - Radar Multi-Feature Graph Representation and Graph Network Fusion Target Detection Methods
JO  - Chinese Journal of Information Fusion
T2  - Chinese Journal of Information Fusion
JF  - Chinese Journal of Information Fusion
VL  - 2
IS  - 1
SP  - 59
EP  - 69
DO  - 10.62762/CJIF.2025.413277
UR  - https://www.icck.org/article/abs/CJIF.2025.413277
KW  - radar target detection
KW  - feature fusion
KW  - graph data
KW  - graph fusion detection network
AB  - 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 feature dimension, detection performance is further improved. Experimental results on dataset composed of real measured IPIX data demonstrate that MFEn-GFDn detection probability is approximately 8% higher than that of the Dual-Channel Convolutional Neural Network (DCCNN). Additionally, MFEn-GFDn enhances detection performance by expanding the feature dimension, particularly in environments lacking corresponding training samples.
SN  - 2998-3371
PB  - Institute of Central Computation and Knowledge
LA  - English
ER  - 

@article{Su2025Radar,
  author = {Ningyuan Su and Xiaolong Chen and Jian Guan and Xinghai Wang and Liangjiang Zhou and Jinhao Wang and Hongyong Wang},
  title = {Radar Multi-Feature Graph Representation and Graph Network Fusion Target Detection Methods},
  journal = {Chinese Journal of Information Fusion},
  year = {2025},
  volume = {2},
  number = {1},
  pages = {59-69},
  doi = {10.62762/CJIF.2025.413277},
  url = {https://www.icck.org/article/abs/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 feature dimension, detection performance is further improved. Experimental results on dataset composed of real measured IPIX data demonstrate that MFEn-GFDn detection probability is approximately 8\% higher than that of the Dual-Channel Convolutional Neural Network (DCCNN). Additionally, MFEn-GFDn enhances detection performance by expanding the feature dimension, particularly in environments lacking corresponding training samples.},
  keywords = {radar target detection, feature fusion, graph data, graph fusion detection network},
  issn = {2998-3371},
  publisher = {Institute of Central Computation and Knowledge}
}