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| Research on Radar Working Mode Recognition Based on Learning Vector Quantization Network |
| DENG Rui1， YAO Zhennan2， WEI Xinyang1， LI Jialin1， MIAO Xishun1 |
| 1. 8511 Research Institute of China Aerospace Science and Industry Corporation， Nanjing 211103， Jiangsu， China；
2. Shanghai Electro-Mechanical Engineering Institute， Shanghai 201109， China |
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Abstract Modern radar working mode recognition demands extremely high real-time performance and accuracy, urgently requiring a lightweight yet efficient classification algorithm. To address this issue, this paper proposes a radar working mode recognition method based on the Learning Vector Quantization (LVQ) network. Firstly, key features are extracted and feature vectors are constructed based on radar signal mechanisms. Secondly, an improved K-means clustering algorithm based on the silhouette coefficient is designed to determine the optimal number of prototypes for each mode category through a data-driven strategy, thereby accurately characterizing its internal multimodal distribution. Finally, an optimized LVQ network is constructed, and a distance threshold discrimination criterion based on statistical analysis is proposed for robust discrimination of unknown radar modes. Simulation results demonstrate that the proposed method achieves an average recognition probability of up to 95% for known radar working modes, and maintains favorable recognition performance under complex scenarios such as noise interference, similar mode confusion, and multi-radar coordination.
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Received: 29 January 2026
Published: 10 July 2026
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2026, Vol. 9 
