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| Neural Network Verification Method for Automatic Target Recognition Based on Multidimensional Radar Echoes |
| XU Qiang1， HUANG Kai2， MA Yuehua2，3， MU Wenpeng1， QUAN Xinyi4， XU Ke1， PAN Jun2，3 |
| 1. School of Computer Science，Shanghai Jiao Tong University， Shanghai 200240， China；
2. Shanghai Electro-Mechanical Engineering Institute， Shanghai 201109，China；
3. Key Laboratory of Automatic Target Recognition（ATR）， Shanghai 201109， China；
4. School of Mathematical Sciences，Shanghai Jiao Tong University， Shanghai 200240， China |
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Abstract Radar target intelligent recognition is a vital research direction in modern military informatization and civilian high-end equipment. In complex electromagnetic environments, the advancement of techniques such as false target jamming and electromagnetic camouflage poses significant challenges to traditional recognition algorithms, leading to feature confusion and elevated false alarm rates. This paper proposes a neural network-based automatic target-jamming recognition method for multidimensional radar echo signals. By constructing a deep detection network model adapted to the temporal characteristics of radar video, the method automatically learns and extracts deep discriminative features of targets and jamming signals from raw radar echo data, achieving efficient differentiation between real targets and jamming. A systematic comparison of different deep learning models is conducted to evaluate their performance differences. Finally, considering practical application requirements and the development trend of deep learning models, future improvement directions for the proposed method in multi-scenario adaptation are discussed.
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Received: 13 June 2025
Published: 10 July 2026
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2026, Vol. 9 
