Physics-Guided Dual-Branch Fusion Model for High-Resolution Range Profile Target Recognition

High-resolution range profile (HRRP) target recognition has advanced with deep learning, yet most existing methods rely primarily on data-driven feature extraction and still face challenges in physical interpretability and noise robustness. To address these issues, this paper proposes a physics-guided dual-branch fusion (PGDBF) model. It consists of two parallel branches: a data-driven branch extracts discriminative features from raw HRRPs, while a physics-guided branch estimates sparse peak parameters (position and intensity) and reconstructs the signal envelope under sparsity constraints. Cross-attention adaptively fuses the two branches. Experiments on measured ten-class aircraft HRRP data show that PGDBF achieves higher accuracy and improved robustness under additive Gaussian noise in the evaluated fixed-route scenario. Visualizations confirm that the estimated peaks align with dominant HRRP energy, linking model variables to physically meaningful peak locations and intensities. These results suggest that integrating explicit peak parameter estimation with data-driven learning is a promising direction for improving HRRP recognition robustness and interpretability under low-SNR conditions.