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SAR Ground Moving Target Refocusing by Combining mRe3 Network and TVβ-LSTM by Yuanyuan Zhou; Jun Shi; Chen Wang; Yao Hu; Zenan Zhou; Xiaqing Yang; Xiaoling Zhang; Shunjun Wei is a Engineering article available to read on EtoBox.

What is SAR Ground Moving Target Refocusing by Combining mRe3 Network and TVβ-LSTM about?

This article proposes a novel framework by combining a modified real-time recurrent regression (mRe 3 ) network and a newly designed trajectory smoothing long short-term memory (LSTM) network for refocusing the ground moving target (GMT) in the synthetic aperture radar (SAR) image. The mRe 3 network that consists of a convolutional neural network (CNN) backbone and two LSTM modules is designed to track the GMT's shadow in an SAR video. Furthermore, we find that the complex trajectory obtained by the tracking network cannot directly be used for refocusing the GMT because of the estimation error. To address the abovementioned problem, a β-order total variation lossbased smoothing LSTM (TV β -LSTM) is proposed to recover the GMT's trajectory to meet the requirement of refocusing. Besides, the effect of TV β on the performance of smoothing LSTM is analyzed. By the experiments on simulated and real SAR videos, we find that the mRe 3 has stronger robustness and a better trajectory reconstruction precision compared with the existing tracking methods, especially for the strong interference cases. In addition, the smoothing LSTM can recover the trajectory of the GMT with higher precision an

Who reads SAR Ground Moving Target Refocusing by Combining mRe3 Network and TVβ-LSTM?

Author

Yuanyuan Zhou; Jun Shi; Chen Wang; Yao Hu; Zenan Zhou; Xiaqing Yang; Xiaoling Zhang; Shunjun Wei

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Published

2021

Language

EN

Field

Engineering (Physical Sciences)