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Статті в журналах з теми "3D ISAR imaging"

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Автор: Grafiati

Опубліковано: 10 грудня 2022

Оновлено: 29 січня 2023

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1

Zhou, Zibo, Libing Jiang, and Zhuang Wang. "A novel image registration method for InISAR 3D imaging." MATEC Web of Conferences 232 (2018): 02044. http://dx.doi.org/10.1051/matecconf/201823202044.

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Анотація:

Image registration is a key intermediate step for Interferometric Inverse Synthetic Aperture Radar (InISAR) three-dimensional (3D) imaging. It arranges the same scatterers of the target on the same pixel cell in different ISAR images, which makes the interferometric processing carried on between the same scatterers to obtain its 3D coordinates. This paper proposes a novel ISAR image registration method of three steps. Firstly, chirp Fourier transform is used to estimate the rotational angular velocity of the target. Secondly, the compensation phase is constructed, according to the rotational angular velocity, to eliminate the wave path difference between different radars echoes. Finally, two-dimensional (2D) Fourier transform is used to yield registered ISAR images. The proposed method achieves the ISAR image registration through phase compensation in echo field, therefore, no extra computation is needed in image field. The experiment results demonstrate the advantages of the proposed method in precision, computation efficiency and practicability.

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2

Zhang, Liqi, and Yanlei Li. "An Image Registration Method Based on Correlation Matching of Dominant Scatters for Distributed Array ISAR." Sensors 22, no. 4 (February 21, 2022): 1681. http://dx.doi.org/10.3390/s22041681.

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Distributed array radar provides new prospects for three-dimensional (3D) inverse synthetic aperture radar (ISAR) imaging. The accuracy of image registration, as an essential part of 3D ISAR imaging, affects the performance of 3D reconstruction. In this paper, the imaging process of distributed array ISAR is proposed according to the imaging model. The ISAR images of distributed array radar at different APCs have different distribution of scatters. When the local distribution of scatters for the same target are quite different, the performance of the existing ISAR image registration methods may not be optimal. Therefore, an image registration method is proposed by integrating the feature-based method and the area-based method. The proposed method consists of two stages: coarse registration and fine registration. In the first stage, a dominant scatters model is established based on scale-invariant feature transform (SIFT). In the second stage, sub-pixel precision registration is achieved using the local correlation matching method. The effectiveness of the proposed method is verified by comparison with other image registration methods. The 3D reconstruction of the registered experimental data is carried out to assess the practicability of the proposed method.

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3

Jiao, Zekun, Chibiao Ding, Longyong Chen, and Fubo Zhang. "Three-Dimensional Imaging Method for Array ISAR Based on Sparse Bayesian Inference." Sensors 18, no. 10 (October 20, 2018): 3563. http://dx.doi.org/10.3390/s18103563.

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Анотація:

The problem of synthesis scatterers in inverse synthetic aperture radar (ISAR) make it difficult to realize high-resolution three-dimensional (3D) imaging. Radar array provides an available solution to this problem, but the resolution is restricted by limited aperture size and number of antennas, leading to deterioration of the 3D imaging performance. To solve these problems, we propose a novel 3D imaging method with an array ISAR system based on sparse Bayesian inference. First, the 3D imaging model using a sparse linear array is introduced. Then the elastic net estimation and Bayesian information criterion are introduced to fulfill model order selection automatically. Finally, the sparse Bayesian inference is adopted to realize super-resolution imaging and to get the 3D image of target of interest. The proposed method is used to process real radar data of a Ku band array ISAR system. The results show that the proposed method can effectively solve the problem of synthesis scatterers and realize super-resolution 3D imaging, which verify the practicality of our proposed method.

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4

Bratchenko, H. D., H. H. Smagliuk, and D. V. Grygoriev. "METHOD FOR ISAR IMAGING OBJECTS WITH 3D ROTATIONAL MOTION." Key title Zbìrnik naukovih pracʹ Odesʹkoï deržavnoï akademìï tehnìčnogo regulûvannâ ta âkostì -, no. 2(9) (2016): 71–78. http://dx.doi.org/10.32684/2412-5288-2016-2-9-71-78.

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5

Martorella, Marco, Daniele Stagliano, Federica Salvetti, and Nicola Battisti. "3D interferometric ISAR imaging of noncooperative targets." IEEE Transactions on Aerospace and Electronic Systems 50, no. 4 (October 2014): 3102–14. http://dx.doi.org/10.1109/taes.2014.130210.

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6

Lv, Qian, and Shaozhe Zhang. "Three-Dimensional Interferometric ISAR Imaging Algorithm Based on Cross Coherence Processing." Sensors 21, no. 15 (July 27, 2021): 5073. http://dx.doi.org/10.3390/s21155073.

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Анотація:

Interferometric inverse synthetic aperture radar (InISAR) has received significant attention in three-dimensional (3D) imaging due to its applications in target classification and recognition. The traditional two-dimensional (2D) ISAR image can be interpreted as a filtered projection of a 3D target’s reflectivity function onto an image plane. Such a plane usually depends on unknown radar-target geometry and dynamics, which results in difficulty interpreting an ISAR image. Using the L-shape InISAR imaging system, this paper proposes a novel 3D target reconstruction algorithm based on Dechirp processing and 2D interferometric ISAR imaging, which can jointly estimate the effective rotation vector and the height of scattering center. In order to consider only the areas of the target with meaningful interferometric phase and mitigate the effects of noise and sidelobes, a special cross-channel coherence-based detector (C3D) is introduced. Compared to the multichannel CLEAN technique, advantages of the C3D include the following: (1) the computational cost is lower without complex iteration and (2) the proposed method, which can avoid propagating errors, is more suitable for a target with multi-scattering points. Moreover, misregistration and its influence on target reconstruction are quantitatively discussed. Theoretical analysis and numerical simulations confirm the suitability of the algorithm for 3D imaging of multi-scattering point targets with high efficiency and demonstrate the reliability and effectiveness of the proposed method in the presence of noise.

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7

Xu, Dan, Bowen Bie, Guang-Cai Sun, Mengdao Xing, and Vito Pascazio. "ISAR Image Matching and Three-Dimensional Scattering Imaging Based on Extracted Dominant Scatterers." Remote Sensing 12, no. 17 (August 20, 2020): 2699. http://dx.doi.org/10.3390/rs12172699.

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Анотація:

This paper studies inverse synthetic aperture radar (ISAR) image matching and three-dimensional (3D) scattering imaging based on extracted dominant scatterers. In the condition of a long baseline between two radars, it is easy for obvious rotation, scale, distortion, and shift to occur between two-dimensional (2D) radar images. These problems lead to the difficulty of radar-image matching, which cannot be resolved by motion compensation and cross-correlation. What is more, due to the anisotropy, existing image-matching algorithms, such as scale invariant feature transform (SIFT), do not adapt to ISAR images very well. In addition, the angle between the target rotation axis and the radar line of sight (LOS) cannot be neglected. If so, the calibration result will be smaller than the real projection size. Furthermore, this angle cannot be estimated by monostatic radar. Therefore, instead of matching image by image, this paper proposes a novel ISAR imaging matching and 3D imaging based on extracted scatterers to deal with these issues. First, taking advantage of ISAR image sparsity, radar images are converted into scattering point sets. Then, a coarse scatterer matching based on the random sampling consistency algorithm (RANSAC) is performed. The scatterer height and accurate affine transformation parameters are estimated iteratively. Based on matched scatterers, information such as the angle and 3D image can be obtained. Finally, experiments based on the electromagnetic simulation software CADFEKO have been conducted to demonstrate the effectiveness of the proposed algorithm.

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8

Mayhan, J. T., M. L. Burrows, K. M. Cuomo, and J. E. Piou. "High resolution 3D "snapshot" ISAR imaging and feature extraction." IEEE Transactions on Aerospace and Electronic Systems 37, no. 2 (April 2001): 630–42. http://dx.doi.org/10.1109/7.937474.

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9

Wang, Xin, and Chao Xuan Shang. "3D Imaging of Bistatic Inverse Synthetic Aperture Radar Based on the Factorization Method." Applied Mechanics and Materials 347-350 (August 2013): 1091–95. http://dx.doi.org/10.4028/www.scientific.net/amm.347-350.1091.

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Анотація:

Based on the geometrical projection of 3D scattering centers on the line of radar sight, a new method of bistatic inverse synthetic aperture radar 3D Imaging is proposed. In the method, Range-Doppler algorithm gives a sequence of 2D images of target during its motion, and 3D reconstruction of target geometry is completed by the factorization method. We analyzed the theory of Bi-ISAR 3D imaging, deduced the process of the factorization method, and introduced the hierarchical reconstruction model. The simulation verified the validity of the paper.

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10

Tian, Biao, Zhejun Lu, Yongxiang Liu, and Xiang Li. "Review on interferometric ISAR 3D imaging: Concept, technology and experiment." Signal Processing 153 (December 2018): 164–87. http://dx.doi.org/10.1016/j.sigpro.2018.07.015.

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11

Wang, Yong, and Yicheng Jiang. "New approach for ISAR imaging of ship target with 3D rotation." Multidimensional Systems and Signal Processing 21, no. 4 (May 5, 2010): 301–18. http://dx.doi.org/10.1007/s11045-010-0111-6.

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12

Xie, Sudao D., M. Y. Pan, and D. S. Li. "Robust method for ship recognition based on ISAR imaging using 3D model." Journal of Engineering 2019, no. 20 (October 1, 2019): 6777–80. http://dx.doi.org/10.1049/joe.2019.0315.

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13

Li, Hongwei, Chao Li, Shiyou Wu, Shen Zheng, and Guangyou Fang. "Adaptive 3D Imaging for Moving Targets Based on a SIMO InISAR Imaging System in 0.2 THz Band." Remote Sensing 13, no. 4 (February 20, 2021): 782. http://dx.doi.org/10.3390/rs13040782.

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Анотація:

Terahertz (THz) imaging technology has received increased attention in recent years and has been widely applied, whereas the three-dimensional (3D) imaging for moving targets remains to be solved. In this paper, an adaptive 3D imaging scheme is proposed based on a single input and multi-output (SIMO) interferometric inverse synthetic aperture radar (InISAR) imaging system to achieve 3D images of moving targets in THz band. With a specially designed SIMO antenna array, the angular information of the targets can be determined using the phase response difference in different receiving channels, which then enables accurate tracking by adaptively adjusting the antenna beam direction. On the basis of stable tracking, the high-resolution imaging can be achieved. A combined motion compensation method is proposed to produce well-focused and coherent inverse synthetic aperture radar (ISAR) images from different channels, based on which the interferometric imaging is performed, thus forming the 3D imaging results. Lastly, proof-of-principle experiments were performed with a 0.2 THz SIMO imaging system, verifying the effectiveness of the proposed scheme. Non-cooperative moving targets were accurately tracked and the 3D images obtained clearly identify the targets. Moreover, the dynamic imaging results of the moving targets were achieved. The promising results demonstrate the superiority of the proposed scheme over the existing THz imaging systems in realizing 3D imaging for moving targets. The proposed scheme shows great potential in detecting and monitoring moving targets with non-cooperative movement, including unmanned military vehicles and space debris.

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14

Li, Yu, Yunhua Zhang, and Xiao Dong. "Squint Model InISAR Imaging Method Based on Reference Interferometric Phase Construction and Coordinate Transformation." Remote Sensing 13, no. 11 (June 7, 2021): 2224. http://dx.doi.org/10.3390/rs13112224.

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Анотація:

The imaging quality of InISAR under squint geometry can be greatly degraded due to the serious interferometric phase ambiguity (InPhaA) and thus result in image distortion problems. Aiming to solve these problems, a three-dimensional InISAR (3D ISAR) imaging method based on reference InPhas construction and coordinate transformation is presented in this paper. First, the target’s 3D coarse location is obtained by the cross-correlation algorithm, and a relatively stronger scatterer is taken as the reference scatterer to construct the reference interferometric phases (InPhas) so as to remove the InPhaA and restore the real InPhas. The selected scatterer needs not to be exactly in the center of the coarsely located target. Then, the image distortion is corrected by coordinate transformation, and finally the 3D coordinates of the target can be accurately estimated. Both simulation and practical experiment results validate the effectiveness of the method.

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15

Lo, Mor Diama, Matthieu Davy, and Laurent Ferro-Famil. "Low-Complexity 3D InISAR Imaging Using a Compressive Hardware Device and a Single Receiver." Sensors 22, no. 15 (August 5, 2022): 5870. http://dx.doi.org/10.3390/s22155870.

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Анотація:

An Interferometric Inverse SAR system is able to perform 3D imaging of non-cooperative targets by measuring their responses over time and through several receiving antennas. Phase differences between signals acquired with a spatial diversity in vertical or horizontal directions are used to localize moving scatterers in 3D. The use of several receiving channels generally results into a costly and complex hardware solution, and this paper proposes performing this multichannel acquisition using a single receiver and a hardware compressive device, based on a chaotic cavity which simultaneously multiplexes in the spectral domain signals acquired over different antennas. The radar responses of the scene are encoded in the spectral domain onto the single output of a leaky chaotic cavity, and can be retrieved by solving an inverse problem involving the random transfer matrix of the cavity. The applicability of this compressed sensing approach for the 3D imaging of a non-cooperative target using low-complexity hardware is demonstrated using both simulations and measurements. This study opens up new perspectives to reduce the hardware complexity of high-resolution ISAR systems.

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16

Nazari, Milad, Ali Mehrpooya, Muhammad Hassan Bastani, Mehdi Nayebi, and Zahra Abbasi. "High‐dimensional sparse recovery using modified generalised SL0 and its application in 3D ISAR imaging." IET Radar, Sonar & Navigation 14, no. 8 (July 6, 2020): 1267–78. http://dx.doi.org/10.1049/iet-rsn.2020.0013.

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17

Yang, Zhijun, Dong Li, Xiaoheng Tan, Hongqing Liu, Yuchuan Liu, and Guisheng Liao. "ISAR Imaging for Maneuvering Targets with Complex Motion Based on Generalized Radon-Fourier Transform and Gradient-Based Descent under Low SNR." Remote Sensing 13, no. 11 (June 4, 2021): 2198. http://dx.doi.org/10.3390/rs13112198.

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Анотація:

The existing inverse synthetic aperture radar (ISAR) imaging algorithms for ship targets with complex three-dimensional (3D) rotational motion are not applicable because of continuous change of image projection plane (IPP), especially under low signal-to-noise-ratio (SNR) condition. To overcome this obstacle, an efficient approach based on generalized Radon Fourier transform (GRFT) and gradient-based descent optimal is proposed in this paper. First, the geometry and signal model for nonstationary IPP of ship targets with complex 3-D rotational motion is established. Furthermore, the two-dimensional (2D) spatial-variant phase errors caused by complex 3-D rotational motion which can seriously blur the imaging performance are derived. Second, to improve the computational efficiency for 2-D spatial-variant phase errors compensation, the coarse motion parameters of ship targets are estimated via the GRFT method. In addition, using the gradient-based descent optimal method, the global optimum solution is iteratively estimated. Meanwhile, to solve the local extremum for cost surface obtained via conventional image entropy, the image entropy combined with subarray averaging is applied to accelerate the global optimal convergence. The main contributions of the proposed method are: (1) the geometry and signal model for ship targets with a complex 3-D rotational motion under nonstationary IPP are established; (2) the image entropy conjunct with subarray averaging operation is proposed to accelerate the global optimal convergence; (3) the proposed method can ensure the imaging accuracy even with high imaging efficiency thanks to the sole optimal solution generated by using the subarray averaging and image entropy. Several experiments using simulated and electromagnetic data are performed to validate the effectiveness of the proposed approach.

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18

Kang, Byung-Soo, Keewoong Lee, and Kyung-Tae Kim. "Image Registration for 3D Interferometric-ISAR Imaging Through Joint-Channel Phase Difference Functions." IEEE Transactions on Aerospace and Electronic Systems, 2020, 1. http://dx.doi.org/10.1109/taes.2020.3021108.

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19

Liu, Afei, Shuanghui Zhang, Chi Zhang, and Yongxiang Liu. "Joint estimation of imaging plane and 3D structure based on ISAR image sequences." Electronics Letters, December 5, 2022. http://dx.doi.org/10.1049/ell2.12688.

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