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Updated: Jun 13, 2026
08:08
Evaluating Targeting Accuracy in the Focal Plane for an Ultrasound-guided High-intensity Focused Ultrasound Phased-array System
Published on: March 6, 2019
Physics-Guided Dual-Branch Fusion Model for High-Resolution Range Profile Target Recognition
Ziheng Xia1,2, Mengdie Wu3, Feng Xiao1
1School of Defence Science and Technology, Xi'an Technological University, Xi'an 710021, China.
Sensors (Basel, Switzerland)
|June 12, 2026
Summary
This study introduces a physics-guided dual-branch fusion model for high-resolution range profile (HRRP) target recognition. The novel approach enhances accuracy and robustness against noise by combining data-driven and physics-based feature extraction.
Area of Science:
- Radar Signal Processing
- Machine Learning for Target Recognition
- Physics-Informed Artificial Intelligence
Background:
- Deep learning enhances high-resolution range profile (HRRP) target recognition but struggles with physical interpretability and noise resilience.
- Existing data-driven methods often lack transparency and robustness in real-world scenarios.
Purpose of the Study:
- To develop a novel physics-guided dual-branch fusion (PGDBF) model for improved HRRP target recognition.
- To address the limitations of current deep learning approaches regarding interpretability and robustness to noise.
Main Methods:
- A dual-branch architecture combining data-driven feature extraction and physics-guided sparse peak parameter estimation.
- Cross-attention mechanism for adaptive fusion of features from both branches.
- Signal envelope reconstruction under sparsity constraints.
Main Results:
- The PGDBF model demonstrated superior accuracy and enhanced robustness against additive Gaussian noise on measured aircraft HRRP data.
- Visualizations confirmed that estimated peaks correspond to dominant HRRP energy, validating physical interpretability.
- The model achieved improved performance in a fixed-route scenario.
Conclusions:
- Integrating explicit peak parameter estimation with data-driven learning offers a promising path for HRRP recognition.
- The PGDBF model improves robustness and interpretability, particularly under low signal-to-noise ratio (SNR) conditions.
- This approach advances the field of radar target recognition by bridging data-driven and physics-based methodologies.
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