TY - GEN
T1 - Sea Clutter Suppression Method Based on Neural Networks
AU - Li, Benben
AU - Qi, Huaiyuan
AU - Tang, Chengkai
AU - Liu, Yang
AU - Gao, Yan
AU - Lian, Jie
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Aiming at the problem of low target signal-To-clutter ratio in the sea clutter environment, a sea clutter suppression method based on deep learning is proposed. Combining with the idea of segmentation and using an improved u-net framework, a network structure for sea clutter suppression is designed. The U-net network is integrated with the residual network, and the Inception module is used in the encoding part to replace the traditional convolution operation. First, the up-And-down sampling structure and jump connection are used to fuse complex multi-layer features. Secondly, by introducing the Inception module, features of different scales and abstract levels are captured, thereby enhancing the representation ability of the coding part. Finally, the actual data is used for the proposed method. Performance is evaluated. The results show that this method has a good effect on improving the target signal-To-clutter ratio and the stability of clutter suppression.
AB - Aiming at the problem of low target signal-To-clutter ratio in the sea clutter environment, a sea clutter suppression method based on deep learning is proposed. Combining with the idea of segmentation and using an improved u-net framework, a network structure for sea clutter suppression is designed. The U-net network is integrated with the residual network, and the Inception module is used in the encoding part to replace the traditional convolution operation. First, the up-And-down sampling structure and jump connection are used to fuse complex multi-layer features. Secondly, by introducing the Inception module, features of different scales and abstract levels are captured, thereby enhancing the representation ability of the coding part. Finally, the actual data is used for the proposed method. Performance is evaluated. The results show that this method has a good effect on improving the target signal-To-clutter ratio and the stability of clutter suppression.
KW - deep learning
KW - inception module
KW - sea clutter suppression
KW - U-net
UR - http://www.scopus.com/inward/record.url?scp=85184855135&partnerID=8YFLogxK
U2 - 10.1109/ICSPCC59353.2023.10400262
DO - 10.1109/ICSPCC59353.2023.10400262
M3 - 会议稿件
AN - SCOPUS:85184855135
T3 - Proceedings of 2023 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2023
BT - Proceedings of 2023 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2023
Y2 - 14 November 2023 through 17 November 2023
ER -