高分辨率雷达成像的流形低秩稀疏张量方法

IF 8.6 1区 地球科学 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC
Gang Xu;Biqin Tan;Chengye Wu;Bangjie Zhang;Hanwen Yu;Mengdao Xing;Wei Hong
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引用次数: 0

摘要

基于压缩感知的高分辨率雷达成像技术在多功能雷达的数据采集负担减轻和资源分配调度等实际应用中具有重要意义。矩阵补全(MC)方法是直接重建稀疏雷达成像中缺失数据的有力工具,克服了传统基于字典的矩阵补全方法误差离散的缺点。在本文中,我们将MC方法扩展到具有多维数据表示的张量补全(TC),并提出了一种新的流形低秩稀疏TC (MLRSTC)雷达成像算法以提高稀疏成像性能。该方案提出了一种吸引张量雷达数据模型,并通过捕获高维的潜在和固有数据结构来发现低秩张量特性。特别是,张量模型的低秩性优越性得到了理论推导和实验分析的证实。然后,将基于kronecker -based -representation (KBR)的张量稀疏性模型应用于所提出的稀疏雷达成像MLRSTC算法的格式,有效地促进了具有增强低秩特性的张量数据的重构。有意义的是,所提出的MLRSTC算法在不同的稀疏数据采样模式下都能很好地工作。其次,在乘法器交替方向法(admm)框架下,通过对所涉及的参数进行闭式更新,以迭代的方式高效求解MLRSTC算法。最后,利用电磁仿真和实测数据进行了实验,验证了所提出的MLRSTC算法超越最先进的SOTA算法的有效性和优越性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Manifold Low Rank and Sparse Tensor Method for High-Resolution Radar Imaging
High-resolution radar imaging with compressive sensing (CS) is significantly important and meaningful in practical applications, such as data collection burden reduction and resource allocation scheduling in a multifunctional radar. The class of matrix completion (MC) methods is a powerful tool to directly reconstruct the missing data to be applied in sparse radar imaging, which can overcome the discrete error drawback of traditional dictionary-based CS methods. In this article, we extend the MC method to tensor completion (TC) with multidimensional data representation, and a novel manifold low-rank and sparse TC (MLRSTC) radar imaging algorithm is proposed for enhanced sparse imaging performance. In the scheme, an attractive tensor radar data model is proposed, and the low-rank tensor property is discovered by capturing the latent and intrinsic data structure in high dimensions. In particular, the low-rankness superiority of the tensor model is confirmed by both the theoretical derivation and experimental analysis. Then, the Kronecker-basis-representation (KBR)-based tensor sparsity model is applied to format the proposed MLRSTC algorithm of sparse radar imaging, which can effectively promote the reconstruction of tensor data with enhanced low-rank property. Meaningfully, the proposed MLRSTC algorithm can work well under the condition of different sparse data sampling patterns. Next, the proposed MLRSTC algorithm is efficiently solved in an iterative manner under the framework of alternating direction method of multipliers (ADMMs) by updating the involved parameters in a closed-form solution. Finally, the experiments using both electromagnetic simulation and measured data are performed to confirm the effectiveness and superiority of the proposed MLRSTC algorithm beyond state-of-the-art (SOTA).
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来源期刊
IEEE Transactions on Geoscience and Remote Sensing
IEEE Transactions on Geoscience and Remote Sensing 工程技术-地球化学与地球物理
CiteScore
11.50
自引率
28.00%
发文量
1912
审稿时长
4.0 months
期刊介绍: IEEE Transactions on Geoscience and Remote Sensing (TGRS) is a monthly publication that focuses on the theory, concepts, and techniques of science and engineering as applied to sensing the land, oceans, atmosphere, and space; and the processing, interpretation, and dissemination of this information.
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