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IEEE Transactions on Antennas and Propagation Information for Authors IEEE天线与传播信息学报
IF 5.8 1区 计算机科学 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2026-01-22 DOI: 10.1109/TAP.2025.3646511
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引用次数: 0
Institutional Listings 机构清单
IF 5.8 1区 计算机科学 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2026-01-22 DOI: 10.1109/TAP.2025.3646513
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引用次数: 0
Distributed Antennas and Near-Field Applications for Future Wireless Systems 分布式天线和未来无线系统的近场应用
IF 5.8 1区 计算机科学 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2026-01-22 DOI: 10.1109/TAP.2025.3646547
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引用次数: 0
List of Reviewers for 2025 2025年评审人员名单
IF 5.8 1区 计算机科学 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2025-12-19 DOI: 10.1109/TAP.2025.3636223
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引用次数: 0
IEEE Transactions on Antennas and Propagation Information for Authors IEEE天线与传播信息学报
IF 5.8 1区 计算机科学 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2025-12-19 DOI: 10.1109/TAP.2025.3640574
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引用次数: 0
Institutional Listings 机构清单
IF 5.8 1区 计算机科学 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2025-12-19 DOI: 10.1109/TAP.2025.3640576
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引用次数: 0
IEEE Transactions on Antennas and Propagation Information for Authors IEEE天线与传播信息学报
IF 5.8 1区 计算机科学 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2025-10-31 DOI: 10.1109/TAP.2025.3622438
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引用次数: 0
Institutional Listings 机构清单
IF 5.8 1区 计算机科学 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2025-10-31 DOI: 10.1109/TAP.2025.3622440
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引用次数: 0
Recent Advances in Synthetic Aperture Antennas: Design, Modelling, and Measurement 合成孔径天线的最新进展:设计、建模和测量
IF 5.8 1区 计算机科学 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2025-10-31 DOI: 10.1109/TAP.2025.3622436
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引用次数: 0
Self-Supervised Enhanced Imaging Network for Sparse Cross MIMO Arrays 稀疏交叉MIMO阵列自监督增强成像网络
IF 5.8 1区 计算机科学 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2025-10-30 DOI: 10.1109/TAP.2025.3625189
Guangnan Xing;Shiyong Li;Ahmad Hoorfar;Qiang An;Guoqiang Zhao;Houjun Sun
In millimeter-wave (MMW) imaging, the focusing performance of sparse multiple-input-multiple-output (MIMO) arrays is susceptible to various noise interferences during the propagation of electromagnetic waves. In addition, the sparse array imaging based on the traditional compressive sensing (CS) algorithms suffers from low computational efficiency due to the numerous iterations, especially in 3-D imaging scenarios. To address these limitations, an interpretable self-supervised network with a parallel structure is proposed for fast enhanced imaging via sparse cross MIMO arrays. The designed interpolation-free MIMO operators are embedded into a generalized complex-valued framework unfolded by the alternating direction method of multipliers (ADMMs) to form the encoder of reconstruction branch, avoiding large-scale MIMO sensing matrix operations and allowing for the fast 3-D imaging processing of sparse MIMO data. The decoder is constructed to map the data from image domain to signal domain, thereby producing the pseudo-labels for self-supervised learning and eliminating the need for costly data annotation. The enhancement branch employs an optimization module based on the complex-valued total variation (CTV) and $l_{1}$ norms to guide the network training, thus improving the noise immunity and imaging performance of the network. The simulations and experiments show that the proposed network can obtain better-focused 3-D imaging results in a short time.
在毫米波成像中,稀疏多输入多输出(MIMO)阵列的聚焦性能容易受到电磁波传播过程中各种噪声干扰的影响。此外,基于传统压缩感知(CS)算法的稀疏阵列成像由于迭代次数多,计算效率较低,尤其是在三维成像场景下。为了解决这些限制,提出了一种具有并行结构的可解释自监督网络,用于通过稀疏交叉MIMO阵列快速增强成像。将设计的无插值MIMO算子嵌入到乘子交替方向法展开的广义复值框架中,形成重构支路的编码器,避免了大规模的MIMO感知矩阵运算,实现了稀疏MIMO数据的快速三维成像处理。该解码器将数据从图像域映射到信号域,从而产生用于自监督学习的伪标签,从而消除了昂贵的数据注释的需要。增强分支采用基于复值总变分(CTV)和$l_{1}$范数的优化模块指导网络训练,从而提高网络的抗噪性和成像性能。仿真和实验表明,该网络能在较短的时间内获得较好的聚焦效果。
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引用次数: 0
期刊
IEEE Transactions on Antennas and Propagation
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