ACCOR: Attention-Enhanced Complex-Valued Contrastive Learning for Occluded Object Classification Using mmWave Radar IQ Signals

Millimeter-wave (mmWave) radar has emerged as a robust sensing modality for several areas, offering reliable operation under adverse environmental conditions. Its ability to penetrate lightweight materials such as packaging or thin walls enables non-visual sensing in industrial and automated environments and can provide robotic platforms with enhanced environmental perception when used alongside optical sensors. Recent work with MIMO mmWave radar has demonstrated its ability to penetrate cardboard packaging for occluded object classification. However, existing models leave room for improvement and warrant a more thorough evaluation across different sensing frequencies. In this paper, we propose ACCOR, an attention-enhanced complex-valued contrastive learning approach for radar, enabling robust occluded object classification. We process complex-valued IQ radar signals using a complex-valued CNN backbone, followed by a multi-head attention layer and a hybrid loss. Our proposed loss combines a weighted cross-entropy term with a supervised contrastive term. We further extend an existing 64 GHz dataset with a 67 GHz subset of the occluded objects and evaluate our model using both center frequencies. Performance evaluation demonstrates that our approach outperforms prior radar-specific models and image classification models with adapted input, achieving classification accuracies of 96.60% at 64 GHz and 93.59% at 67 GHz for ten different objects. These results demonstrate the benefits of complex-valued deep learning with attention and contrastive learning for mmWave radar-based occluded object classification in industrial and automated environments.