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| Research on Target Recognition Method Based on Multi-Source Information Fusion |
| CONG Xiaoyu1， YANG Jiayi2，3， SHAN Shichen4， ZUO Qian1 |
| 1. School of Information and Artificial Intelligence， Yangzhou University， Yangzhou 225127， Jiangsu， China；
2. National Key Laboratory of Automatic Target Recognition （Shanghai）， Shanghai 201109， China；
3. Shanghai Electro-Mechanical Engineering Institute， Shanghai 201109， China；
4. Sitemax Electronic Technology Co.， Ltd.， Shanghai 200233， China |
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Abstract During multi-source information fusion, efficiently combining features from High Resolution Range Profiles (HRRP) and Inverse Synthetic Aperture Radar (ISAR) images is challenging due to the difficulty in fusion, limited sample availability, and the open-set recognition problem. To address these issues, a spatial target recognition method based on feature alignment and data augmentation was proposed. Firstly, Principal Component Analysis (PCA) was adopted to reduce the dimensionality of HRRP and extract time-frequency features. The Pauli decomposition was then utilised to expand the data of polarised ISAR images. After that, an improved ResNet18 network and a Transformer fusion module were constructed to align and fuse HRRP and ISAR features. Finally, the OpenMax open-set recognition framework was introduced, and the Weibull distribution was used to model class boundaries to achieve discrimination of unknown classes. Experimental results show that the proposed method achieves 90.43% accuracy in closed-set recognition and 91.39% rejection rate for unknown classes in open-set recognition, verifying its effective recognition and generalisation abilities in complex scenarios.
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Received: 05 December 2025
Published: 11 March 2026
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2026, Vol. 9 
