Relevant bibliographies by topics / 3D ISAR imaging

Academic literature on the topic '3D ISAR imaging'

Author: Grafiati
Published: 10 December 2022
Last updated: 29 January 2023

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Journal articles on the topic "3D ISAR imaging"

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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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Dissertations / Theses on the topic "3D ISAR imaging"

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Brinkman, Wade H. "Focusing ISAR images using fast adaptive time-frequency and 3D motion detection on simulated and experimental radar data." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2005. http://library.nps.navy.mil/uhtbin/hyperion/05Jun%5FBrinkman.pdf.

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Thesis (M.S. in Electrical Engineering)--Naval Postgraduate School, June 2005.
Thesis Advisor(s): Michael A. Morgan, Thayananthan Thayaparan. Includes bibliographical references (p. 119-120). Also available online.
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Salvetti, Federica. "Multi-channel techniques for 3D ISAR." Thesis, 2015. http://hdl.handle.net/2440/98002.

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This thesis deals with the challenge of forming 3D target reconstruction by using spatial multi-channel ISAR configurations. The standard output of an ISAR imaging system is a 2D projection of the true three-dimensional target reflectivity onto an image plane. The orientation of the image plane cannot be predicted a priori as it strongly depends on the radar-target geometry and on the target motion, which is typically unknown. This leads to a difficult interpretation of the ISAR images. In this scenario, this thesis aim to give possible solutions to such problems by proposing three 3D processing based on interferometry, beamforming techniques and MIMO InISAR systems. The CLEAN method for scattering centres extraction is extended to multichannel ISAR systems and a multistatic 3D target reconstruction that is based on a incoherent technique is suggested.
Thesis (Ph.D.) -- University of Adelaide, School of Electrical and Electronic Engineering, 2015.
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Book chapters on the topic "3D ISAR imaging"

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Zhao, Jingcheng, Zongkai Yang, and Shaozhu Gu. "Compressed Sensing ISAR 3D Imaging Methods Based on OMP Algorithm." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 279–92. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-22968-9_25.

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"3D Interferometric ISAR." In Radar Imaging for Maritime Observation, 175–212. Boca Raton, FL : Taylor & Francis, 2016. | Series: Signal and: CRC Press, 2018. http://dx.doi.org/10.1201/9781315374253-15.

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"Multistatic 3D ISAR imaging of maritime targets." In Multidimensional Radar Imaging, 287–309. Institution of Engineering and Technology, 2019. http://dx.doi.org/10.1049/sbra527e_ch9.

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Conference papers on the topic "3D ISAR imaging"

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Kempf, Timo, Markus Peichl, Stephan Dill, and Helmut Suess. "3D Tower-Turntable ISAR Imaging." In 2007 European Radar Conference. IEEE, 2007. http://dx.doi.org/10.1109/eurad.2007.4404950.

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Goyette, Thomas M., William E. Nixon, Jason C. Dickinson, Ryan H. Wetherbee, Jason D. Cook, and Andrew J. Gatesman. "3D radar imaging using interferometric ISAR." In Radar Sensor Technology XXII, edited by Kenneth I. Ranney and Armin Doerry. SPIE, 2018. http://dx.doi.org/10.1117/12.2304438.

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Giusti, Elisa, Selenia Ghio, and Marco Martorella. "Drone-based 3D interferometric ISAR Imaging." In 2021 IEEE Radar Conference (RadarConf21). IEEE, 2021. http://dx.doi.org/10.1109/radarconf2147009.2021.9455271.

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Cai, Jinjian, Marco Martorella, Quanhua Liu, Zegang Ding, and Elisa Giusti. "3D ISAR Imaging: The Alignment Problem." In 2019 International Radar Conference (RADAR). IEEE, 2019. http://dx.doi.org/10.1109/radar41533.2019.171419.

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Cai, Jinjian, Marco Martorella, Aifang Liu, Elisa Giusti, Zuzhen Huang, and Long Huang. "3D ISAR Imaging: ATR Based on the Alignment Between 3D ISAR Reconstruction and CAD Model." In 2022 23rd International Radar Symposium (IRS). IEEE, 2022. http://dx.doi.org/10.23919/irs54158.2022.9905008.

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Fontana, Anna, Patrick Berens, Daniele Stagliano, and Marco Martorella. "3D ISAR/SAR imaging using multichannel real data." In 2016 IEEE Radar Conference (RadarConf16). IEEE, 2016. http://dx.doi.org/10.1109/radar.2016.7485118.

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Cai, J., M. Martorella, J. Guo, Q. Liu, Z. Ding, and E. Giusti. "3D ISAR imaging: Multi-view image fusion problem." In IET International Radar Conference (IET IRC 2020). Institution of Engineering and Technology, 2021. http://dx.doi.org/10.1049/icp.2021.0620.

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Xiaochun, Xie, and Zhang Yunhua. "3D ISAR imaging based on MIMO radar array." In 2009 2nd Asian-Pacific Conference on Synthetic Aperture Radar (APSAR). IEEE, 2009. http://dx.doi.org/10.1109/apsar.2009.5374254.

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Thayaparan, T., and W. Brinkman. "3D motion model and detection method for ISAR imaging." In 2006 International Radar Symposium. IEEE, 2006. http://dx.doi.org/10.1109/irs.2006.4338117.

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Hu, Xiaowei, Ningning Tong, Yongshun Zhang, and Yuchen Wang. "3D imaging using narrowband MIMO radar and ISAR technique." In 2015 International Conference on Wireless Communications & Signal Processing (WCSP). IEEE, 2015. http://dx.doi.org/10.1109/wcsp.2015.7340990.

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Reports on the topic "3D ISAR imaging"

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Mayhan, Joseph T. Phase-Enhanced" 3D Snapshot ISAR Imaging and Interferometric SAR". Fort Belvoir, VA: Defense Technical Information Center, December 2009. http://dx.doi.org/10.21236/ada511473.

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