Transformer-based neural network for enhanced radar target classification and intent recognition
TL;DR
This paper proposes a novel Transformer-based neural network architecture specifically designed for radar signal processing that integrates multi-head self-attention mechanisms with temporal convolutional networks to effectively model both local patterns and global dependencies in radar data.
Abstract
Radar-based target classification and intent recognition play crucial roles in autonomous driving, surveillance, and aerospace applications. Traditional methods relying on handcrafted features and conventional deep learning architectures often fail to capture long-range dependencies in radar sequences. This paper proposes a novel Transformer-based neural network architecture specifically designed for radar signal processing. Our approach integrates multi-head self-attention mechanisms with temporal convolutional networks to effectively model both local patterns and global dependencies in radar data. We design a hierarchical feature extraction module that processes range-Doppler maps and micro-Doppler signatures for robust target classification. Furthermore, we introduce an intent recognition module that leverages trajectory prediction and behavioral pattern analysis. Extensive experiments on simulated and real radar datasets demonstrate that our method achieves 94.7% classification accuracy and 89.3% intent recognition accuracy, outperforming state-of-the-art methods including CNNbased and LSTM-based approaches. The proposed architecture also shows superior computational efficiency with 35% fewer parameters compared to baseline models.