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Multichannel SAR Moving Target Detection via Deep Learned RPCA-Net.pdf (473.24 kB)

Multi-Channel SAR Moving Target Detection via RPCA-Net

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journal contribution
posted on 2022-12-07, 16:09 authored by X Zhang, D Wu, D Zhu, Huiyu Zhou

Ground moving target indication (GMTI), as a challenging task for synthetic aperture radar (SAR) systems, keeps drawing considerable attention. Robust principal component analysis (RPCA) aiming at separating low-rank and sparse components has been successfully employed in SAR systems for GMTI recently. However, its practical application is limited by the heavy computational burden as well as the requirement of manual parameter modification. To cope with this problem, a fast and free of presetting parameters RPCA network (RPCA-Net) is proposed for SAR-GMTI under strong clutter background. In the proposed method, a novel RPCA model is first introduced, where not only the low-rank and sparse terms but also the errors in practical SAR systems are taken into account. Moreover, the low-rank factorization plus scaled gradient descent (ScaledGD) is also employed to acquire low-rank clutter background rather than singular value decomposition (SVD). Then, we parameterize our proposed RPCA model and unfold it as a feedforward neural network (FNN) to acquire the iterative parameters through backpropagation. Compared to the GMTI methods based on traditional RPCA models, our proposed RPCA-Net can provide a higher detection ability and faster convergence without presetting parameters empirically. Experiments on two groups of measured data collected by airborne SAR systems validate the superior performance of the proposed RPCA-Net.

Funding

10.13039/501100001809-National Natural Science Foundation of China (Grant Number: 62271252 and 62101248)

10.13039/501100004608-Natural Science Fund of Jiangsu Province (Grant Number: BK20210282)

History

Author affiliation

School of Computing and Mathematical Sciences, University of Leicester

Version

  • AM (Accepted Manuscript)

Published in

IEEE Geoscience and Remote Sensing Letters

Volume

19

Publisher

Institute of Electrical and Electronics Engineers

issn

1545-598X

Copyright date

2022

Available date

2022-12-07

Language

en

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