基于深度金字塔残差收缩网络的雷达目标探测多尺度特征学习

IF 5.1 2区计算机科学 Q1 ENGINEERING, AEROSPACE IEEE Transactions on Aerospace and Electronic Systems Pub Date : 2024-10-30 DOI:10.1109/taes.2024.3488675

Boyu Wang, Gongjian Zhou

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Multiscale Feature Learning Based on Deep Pyramid Residual Shrinking Network for Radar Target Detection

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来源期刊

IEEE Transactions on Aerospace and Electronic Systems 工程技术-电信学

CiteScore

7.80

自引率

13.60%

发文量

433

审稿时长

8.7 months

期刊介绍： IEEE Transactions on Aerospace and Electronic Systems focuses on the organization, design, development, integration, and operation of complex systems for space, air, ocean, or ground environment. These systems include, but are not limited to, navigation, avionics, spacecraft, aerospace power, radar, sonar, telemetry, defense, transportation, automated testing, and command and control.

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