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High-resolution ISAR imaging via MMV-based block-sparse signal recovery

IET RADAR SONAR AND NAVIGATION 2019年第2期13卷 208-212页

作者： He, Xingyu Tong, Ningning Hu, Xiaowei Air Force Engn Univ Air & Missile Def Coll Xian 710051 Shaanxi Peoples R China

Recently, compressed sensing (CS) methods are widely used in high-resolution inverse synthetic aperture radar (ISAR) imaging. However, these CS-based imaging methods generally do not take the block-sparse structure of the ISAR images into account, and the image recovery performance needs to be improved. By utilising the block-sparse structure of the signal, more sparse solution and better focused ISAR images can be obtained. In this study, the authors convert the block-sparse signal recovery problem into a sparse recovery problem for multiple measurement vector (MMV), which can be solved more efficiently. A sparser and more accurate solution can be obtained based on the MMV model, and therefore better focused ISAR images can be recovered. Simulation and experimental results validate the effectiveness of the proposed method.

关键词： radar imaging compressed sensing image reconstruction image resolution signal reconstruction vectors synthetic aperture radar compressed sensing methods high-resolution inverse synthetic aperture radar imaging CS-based block-sparse structure image recovery performance sparse solution focused ISAR images block-sparse signal recovery problem sparse recovery problem high-resolution ISAR imaging MMV-based block-sparse signal recovery

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Exploiting angular spread in channel estimation of millimeter wave MIMO system

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IET COMMUNICATIONS 2022年第3期16卷 195-205页

作者： You, You Zhang, Li Univ Leeds Sch Elect & Elect Engn Leeds LS2 9JT W Yorkshire England Purple Mt Labs Nanjing Peoples R China

Millimeter wave (mmWave) frequency spectrum can mitigate severe spectrum shortage caused by the explosive growth of mobile data demand. To overcome the high propagation loss of mmWave signals, massive multi-input multi-output (MIMO) and hybrid architecture are employed. As in microwave communication systems, channel state information (CSI) is essential to fully achieve the advantages of mmWave communication. However, due to the massive number of antennas and hybrid architecture, the CSI acquisition is challenging. The sparsity of mmWave channel can be utilized to reduce the training overhead. In addition to sparsity, real-world measurements in dense urban propagation environments reveal that the mmWave channel may spread in form of cluster of paths over the angular domains, namely the angular spread. In this paper, it is utilized to formulate the channel estimation as a block-sparse signal recovery problem. The block orthogonal matching pursuit (BOMP) is used to validate the model. Then, block fast Bayesian matching pursuit (BFBMP) algorithm is proposed to solve the above problem. Compared with other existing channel estimation methods, simulation results show that the angular spread feature and the proposed BFBMP can considerably improve the CSI estimation with less complexity.

关键词： hybrid architecture state information millimeter wave frequency spectrum Bayes methods angular spread feature block orthogonal matching pursuit high propagation loss CSI acquisition wireless channels MIMO communication existing channel estimation methods mmWave signals time-frequency analysis millimetre wave communication Bayesian matching pursuit algorithm mmWave communication channel estimation massive multiinput multioutput severe spectrum shortage millimeter wave MIMO system real-world measurements 5G mobile communication angular domains iterative methods mobile data demand compressed sensing CSI estimation microwave communication systems mmWave channel block-sparse signal recovery problem dense urban propagation environments

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Moving target inverse synthetic aperture radar image resolution enhancement based on two-dimensional block sparse signal reconstruction

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IET IMAGE PROCESSING 2021年第13期15卷 3153-3159页

作者： He, Xingyu Tong, Ningning Liu, Tao Air Force Engn Univ Air Traff Control & Nav Coll Xian 710051 Shaanxi Peoples R China Air Force Engn Univ Air & Missile Def Coll Xian Shaanxi Peoples R China Air Force Engn Univ Teaching & Res Guarantee Ctr Xian Shaanxi Peoples R China

To achieve the high-resolution inverse synthetic aperture radar (ISAR) imaging of moving targets, the range Doppler method and compressed sensing technique can be used. However, those methods are generally faced with the problem of migration through range cells and basis mismatch problems, and the imaging results can be improved. To solve these problems and improve the image quality, the problem of ISAR imaging is termed as a block-sparse signal recovery problem by utilizing the block-sparse structure of the ISAR images. A localized low-rank promoting (LLP) method is introduced and extended to the complex case for the recovery of range compressed block-sparse signals. The sparse recovery problem is solved by minimizing another function, which is the surrogate function that can be solved more effectively. Based on the LLP method, the coefficients of the range compressed echo signal are reconstructed and some 2 x 2 matrices can be obtained. And then the log-determinant function is introduced to find the low rankness solutions for these matrices. Then the LLP method is also used in the cross-range domain to reconstruct the ISAR image. Experimental results show that the proposed method can recover better focused and higher quality ISAR images compared with the traditional methods.

关键词： image resolution range compressed echo signal range compressed block-sparse signals compressed sensing Radar equipment, systems and applications image quality LLP method two-dimensional block sparse signal reconstruction image enhancement Optimisation techniques ISAR imaging range Doppler method Image and video coding high-resolution inverse synthetic aperture radar imaging sparse recovery problem higher quality ISAR images moving targets range cells block-sparse signal recovery problem matrix algebra Interpolation and function approximation (numerical analysis) ISAR image block-sparse structure synthetic aperture radar radar imaging target inverse synthetic aperture radar image resolution enhancement basis mismatch problems cross-range domain localized low-rank promoting method

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