Supervised contrastive deep Q-Network for imbalanced radar automatic target recognition

doi:10.1016/j.patcog.2024.111264

Supervised contrastive deep Q-Network for imbalanced radar automatic target recognition

2025-05-01

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DOI：10.1016/j.patcog.2024.111264

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In the presence of limited and extremely imbalanced data, deep learning methods for radar automatic target recognition (RATR) often suffer from significant performance degradation and overfitting. To tackle this issue, we propose S upervised C ontrastive D eep Q-network (SCDQ), a novel end-to-end reinforcement learning method, for multi-class imbalanced RATR. SCDQ formulates the imbalanced recognition problem as a Markov decision process (MDP) and optimizes the classifier through an enhanced Q-learning paradigm. In order to augment the model's feature extraction capabilities under the constraint of limited samples, we tightly integrate reinforcement learning (RL) with supervised contrastive learning, introducing an innovative feature enhancement module. To further enhance the model's adaptability to challenging samples, we integrate a meticulously designed priority sampling into the proposed SCDQ framework, denoted as SCDQ-P. Experimental results on both simulated and real datasets demonstrate the reliability and effectiveness of the proposed method.

Keywords:

Imbalanced RATR

Deep learning

Deep reinforcement learning (DRL)

Supervised contrastive learning

Priority sampling

Supervised contrastive deep Q-Network for imbalanced radar automatic target recognition

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