Peng Gao
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Peng Gao has a total of 5 co-authors. Here are the top co-authors by number of shared publications:
- Jihao Zhang
School of Mechatronical Engineering, Beijing Institute of Technology, Beijing, China, China
1 shared publication - Peng Gong
School of Mechatronical Engineering, Beijing Institute of Technology, Beijing, China
1 shared publication - Xiang Gao
School of Mechatronical Engineering, Beijing Institute of Technology, Beijing, China
1 shared publication - Zeyu Jin
School of Mechatronical Engineering, Beijing Institute of Technology, Beijing, China, China
1 shared publication - Zijiao Zhou
School of Mechatronical Engineering, Beijing Institute of Technology, Beijing, China, China
1 shared publication
Latest Publications
- Open Access
- 12 November 2026
- Published by MDPI AG in Preprints.org
A Cross-Domain Data Generalization Algorithm for Radio Detector Anti-Jamming Based on SA-PCGAN
Radio detectors operating in complex electromagnetic environments face distribution shifts between controlled laboratory and field measurements. This study proposes a Structure-Aware and Physics-Constrained Generative Adversarial Network (SA-PCGAN) for cross-domain time–frequency data generation. The model combines self-attention for global feature association with an L1-norm alignment constraint to preserve dominant signal structures while learning field background characteristics. Training uses 10,000 pairs of measured time–frequency images, and downstream classification is evaluated using a separate test set of 1,500 measured field samples. In the internal ablation study, the complete model achieves a time–frequency Pearson correlation coefficient of 0.7544 and a Fréchet Inception distance (FID) of 109.76. In the external benchmark comparison, the FID is 273.41 and the distributional maximum mean discrepancy is 0.181606. A ResNet50 classifier trained with SA-PCGAN-augmented data achieves 93.00% accuracy; in the separate ResNet18 ablation setting, accuracy reaches 92.27%. These results indicate that, under the datasets and experimental conditions considered, SA-PCGAN improves generated data quality and their utility for downstream recognition. The method provides a basis for cross-environment data augmentation and performance evaluation of radio detectors.
- 12 November 2026
- Published by MDPI AG in Preprints.org
Open AccessA Cross-Domain Data Generalization Algorithm for Radio Detector Anti-Jamming Based on SA-PCGAN
Radio detectors operating in complex electromagnetic environments face distribution shifts between controlled laboratory and field measurements. This study proposes a Structure-Aware and Physics-Constrained Generative Adversarial Network (SA-PCGAN) for cross-domain time–frequency data generation. The model combines self-attention for global feature association with an L1-norm alignment constraint to preserve dominant signal structures while learning field background characteristics. Training uses 10,000 pairs of measured time–frequency images, and downstream classification is evaluated using a separate test set of 1,500 measured field samples. In the internal ablation study, the complete model achieves a time–frequency Pearson correlation coefficient of 0.7544 and a Fréchet Inception distance (FID) of 109.76. In the external benchmark comparison, the FID is 273.41 and the distributional maximum mean discrepancy is 0.181606. A ResNet50 classifier trained with SA-PCGAN-augmented data achieves 93.00% accuracy; in the separate ResNet18 ablation setting, accuracy reaches 92.27%. These results indicate that, under the datasets and experimental conditions considered, SA-PCGAN improves generated data quality and their utility for downstream recognition. The method provides a basis for cross-environment data augmentation and performance evaluation of radio detectors.