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Recently, radar high-resolution range profile (HRRP) recognition based on convolutional neural networks (CNNs) has received considerable attention due to its robustness to translation and amplitude changes. Most of the existing methods require that sufficient labeled data with complete aspect angles be used as training data, which is a difficult task in practice. In addition, HRRP signals have a high sensitivity to the aspect angle. Therefore, the representative and discriminative powers of the features extracted from typical CNN models are reduced due to incomplete aspect angles in the training data, which significantly limit the recognition performance. This paper first considers the problem of HRRP recognition with incomplete aspect angle training data and addresses the problem by a deep transfer learning framework. Specifically, the two proposed methods enhance the recognition performance by exploring the discriminative power and the intraclass consistency with auxiliary data, which have HRRP signals with complete aspect angles. This paper generates a simulated HRRP dataset from public data to validate the proposed work. The comparisons of the recognition results demonstrate that the proposed framework outperforms the latest CNN-based models. INDEX TERMS High-resolution range profile, target recognition, deep learning, transfer learning. YI WEN received the B.S. degree from Xiamen University, Xiamen, China, in 2018, where she is currently pursuing the master's degree with the Department of Communication Engineering, School of Information Science and Engineering. Her main research interests include deep learning and signal processing. LIANGCHAO SHI received the B.S. degree from Xiamen University, Xiamen, China, in 2018, where he is currently pursuing the master's degree with the Department of Communication Engineering, School of Information Science and Engineering. His main research interests include deep learning and signal processing.

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In this paper, aluminum coatings were prepared on a steel substrate by thermal spraying, and the corrosion morphology and corrosion resistance of the coating were investigated by salt spray and immersion tests. The results showed that after three months of salt spray tests, the coating still exhibited a surface morphology without significant damage and had good damage tolerance. Further effective protection of the substrate can be achieved by spraying the coating surface with paint. After three months of immersion test, the corrosion rate of samples with thicker coatings was located between 0.002 mm/y and 0.005 mm/y, and only a small amount of corrosion products was observed on the coating surface. The coated samples after salt spray and immersion tests maintained sufficient adhesion (17.07 MPa and 19.25 MPa), and the surface aluminum coating was highly reliable for protection of the steel substrate. In general, the reliability of the coating can be further improved by painting the surface of the thicker Al coating. This provides more ideas for the protection of transmission and transformation equipment.