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Mobile Multimedia Computing in Cyber-Physical Surveillance Services Through UAV-Borne Video-SAR: A Taxonomy of Intelligent Data Processing for IoMT-Enabled Radar Sensor Networks
Keywords:
high-performance computing, Video Synthetic Aperture Radar (Video-SAR) imaging, radar networks, radar image processing, Internet of Multimedia Things (IoMT), cybersecurity
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Khosravi MR, Samadi S.
Mobile Multimedia Computing in Cyber-Physical Surveillance Services Through UAV-Borne Video-SAR: A Taxonomy of Intelligent Data Processing for IoMT-Enabled Radar Sensor Networks.
Tsinghua Science and Technology,
2022, 27(2): 288-302.
https://doi.org/10.26599/TST.2021.9010013
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This study investigates the different aspects of multimedia computing in Video Synthetic Aperture Radar (Video-SAR) as a new mode of radar imaging for real-time remote sensing and surveillance. This research also considers new suggestions in the systematic design, research taxonomy, and future trends of radar data processing. Despite the conventional modes of SAR imaging, Video-SAR can generate video sequences to obtain online monitoring and green surveillance throughout the day and night (regardless of light sources) in all weathers. First, an introduction to Video-SAR is presented. Then, some specific properties of this imaging mode are reviewed. Particularly, this research covers one of the most important aspects of the Video-SAR systems, namely, the systematic design requirements, and also some new types of visual distortions which are different from the distortions, artifacts and noises observed in the conventional imaging radar. In addition, some topics on the general features and high-performance computing of Video-SAR towards radar communications through Unmanned Aerial Vehicle (UAV) platforms, Internet of Multimedia Things (IoMT), Video-SAR data processing issues, and real-world applications are investigated.
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Mobile Multimedia Computing in Cyber-Physical Surveillance Services Through UAV-Borne Video-SAR: A Taxonomy of Intelligent Data Processing for IoMT-Enabled Radar Sensor Networks
Abstract
This study investigates the different aspects of multimedia computing in Video Synthetic Aperture Radar (Video-SAR) as a new mode of radar imaging for real-time remote sensing and surveillance. This research also considers new suggestions in the systematic design, research taxonomy, and future trends of radar data processing. Despite the conventional modes of SAR imaging, Video-SAR can generate video sequences to obtain online monitoring and green surveillance throughout the day and night (regardless of light sources) in all weathers. First, an introduction to Video-SAR is presented. Then, some specific properties of this imaging mode are reviewed. Particularly, this research covers one of the most important aspects of the Video-SAR systems, namely, the systematic design requirements, and also some new types of visual distortions which are different from the distortions, artifacts and noises observed in the conventional imaging radar. In addition, some topics on the general features and high-performance computing of Video-SAR towards radar communications through Unmanned Aerial Vehicle (UAV) platforms, Internet of Multimedia Things (IoMT), Video-SAR data processing issues, and real-world applications are investigated.
Keywords:
high-performance computing, Video Synthetic Aperture Radar (Video-SAR) imaging, radar networks, radar image processing, Internet of Multimedia Things (IoMT), cybersecurity
References(60)
[1]
M. R. Khosravi and S. Samadi, Data compression in ViSAR sensor networks using non-linear adaptive weighting, EURASIP Journal on Wireless Communications and Networking, , 2019.
[2]
B. Bahri-Aliabadi, M. R. Khosravi, and S. Samadi, Frame rate computing in video SAR using geometrical analysis, in Proc. 24th Int’l Conf. Parallel and Distributed Proc. Techniques and Applications (PDPTA’18), Las Vegas, NV, USA, 2018, pp. 165-167.
[3]
Sandia National Lab, https://www.sandia.gov/radar/video/index.html, 2020.
[4]
A. Damini, B. Balaji, C. Parrya, and V. Mantle, A VideoSAR mode for the x-band wideband experimental airborne radar, in Proc. SPIE 7699, Int. Society for Optical Engineering, Orlando, FL, USA, 2010, p. 76990E.
[5]
S. Kim and M. H. Ka, SAR raw data simulation for multiple-input multiple-output video synthetic aperture radar using beat frequency division frequency modulated continuous wave, Microw. Opt. Technol. Lett., vol. 61, no. 5, pp. 1411-1418, 2019.
[6]
R. Z. Hu, R. Min, and Y. M. Pi, A Video-SAR imaging technique for aspect-dependent scattering in wide angle, IEEE Sens. J., vol. 17, no. 12, pp. 3677-3688, 2017.
[7]
X. S. Song and W. D. Yu, Processing video-SAR data with the fast backprojection method, IEEE Trans. Aerosp. Electron. Syst., vol. 52, no. 6, pp. 2838-2848, 2016.
[8]
Y. Zhang, D. Y. Zhu, X. H. Mao, and W. W. Jin, Location displacement analysis on target height in VideoSAR image sequence, J. Eng., vol. 2019, no. 19, pp. 5584-5587, 2019.
[9]
S. H. Kim, R. Fan, and F. Dominski, ViSAR: A 235 GHz radar for airborne applications, in Proc. IEEE Radar Conf., Oklahoma City, OK, USA, 2018, pp. 1549-1554.
[10]
D. A. Yocky, R. D. West, R. M. Riley, and T. M. Calloway, Monitoring surface phenomena created by an underground chemical explosion using fully polarimetric VideoSAR, IEEE Trans. Geosci. Remote Sens., vol. 57, no. 5, pp. 2481-2493, 2019.
[11]
M. R. Khosravi and S. Samadi, Reliable data aggregation in internet of ViSAR vehicles using chained dual-phase adaptive interpolation and data embedding, IEEE Int. Things J., vol. 7, no. 4, pp. 2603-2610, 2020.
[12]
M. R. Khosravi and S. Samadi, Efficient payload communications for IoT-enabled ViSAR vehicles using discrete cosine transform-based quasi-sparse bit injection, EURASIP J. Wirel. Commun. Network., vol. 2019, no. 1, p. 262, 2019.
[13]
M. R. Khosravi and S. Samadi, Modified data aggregation for aerial ViSAR sensor networks in transform domain, in Proc. 25th Int’l Conf. Parallel and Distributed Processing Techniques and Applications (PDPTA’19), Las Vegas, NV, USA, 2019, pp. 87-90.
[14]
M. R. Khosravi, S. Samadi, and R. Mohseni, Spatial interpolators for intra-frame resampling of SAR videos: A comparative study using real-time HD, medical and radar data, Curr. Signal Transduct. Ther., vol. 15, no. 2, pp. 136-188, 2020.
[15]
U.S. Department of Energy, https://www.energy.gov/eere/solar/project-pro?le-national-solar-thermal-test-facility, 2020.
[16]
C. E. Covault, L. M. Boone, D. Bramel, E. Chae, P. Fortin, D. Gingrich, D. S. Hanna, J. A. Hinton, C. Meuller, R. Mukherjee, et al., The status of the STACEE observatory, in Proc. 27th Int. Cosmic Ray Conf., Hamburg, Germany, 2001, pp. 1-4.
[17]
S. T. Zhao, J. Chen, W. Yang, B. Sun, and Y. M. Wang, Image formation method for spaceborne video SAR, in Proc. of 2015 IEEE 5th Asia-Pacific Conf. Synthetic Aperture Radar (APSAR), Singapore, 2015, pp. 148-151.
[18]
J. Miller, E. Bishop, and A. Doerry, An application of backprojection for video SAR image formation exploiting a subaperature circular shift register, in Proc. 8746, Algorithms for Synthetic Aperture Radar Imagery XX, Baltimore, MD, USA, 2013, p. 874609, 2013.
[19]
Y. Zhang, X. H. Mao, H. Yan, D. Y. Zhu, and X. C. Hu, A novel approach to moving targets shadow detection in VideoSAR imagery sequence, in Proc. of 2017 IEEE Int. Geoscience and Remote Sensing Symp. (IGARSS), Fort Worth, TX, USA, 2017, pp. 606-609.
[20]
H. B. Wallace, Development of a video SAR for FMV through clouds, in Proc. 9479, Open Architecture/Open Business Model Net-Centric Systems and Defense Transformation 2015, Baltimore, MD, USA, 2015, p. 91790L.
[21]
H. Yan, X. H. Mao, J. D. Zhang, and D. Y. Zhu, Frame rate analysis of video synthetic aperture radar (ViSAR), in Proc. ISAP2016, Okinawa, Japan, 2016, pp. 446-447.
[22]
B. Liu, X. P. Zhang, K. Tang, M. Liu, and L. Liu, Spaceborne video-sar moving target surveillance system, in Proc. of 2016 IEEE Int. Geoscience and Remote Sensing Symp. (IGARSS), Beijing, China, 2016, pp. 2348-2351.
[23]
Y. Zhang and D. Y. Zhu, Height retrieval in postprocessing-based VideoSAR image sequence using shadow information, IEEE Sens. J., vol. 18, no. 19, pp. 8108-8116, 2018.
[24]
J. Liang, R. N. Zhang, L. X. Ma, Z. Lv, K. Jiao, D. W. Wang, and Z. Y. Tan, An efficient image formation algorithm for spaceborne video SAR, in Proc. of IGARSS 2018-2018 IEEE Int. Geoscience and Remote Sensing Symp., Valencia, Spain, 2018, pp. 3675-3678.
[25]
M. R. Khosravi and S. Samadi, BL-ALM: A blind scalable edge-guided reconstruction filter for smart environmental monitoring through green IoMT-UAV networks, IEEE Transactions on Green Communications and Networking, vol. 5, no. 2, pp. 727-736, 2021.
[26]
M. R. Khosravi and S. Samadi, Frame rate computing and aggregation measurement toward QoS/QoE in Video-SAR systems for UAV-borne real-time remote sensing, The Journal of Supercomputing, , 2021,
[27]
D. Wang, D. Y. Zhu, and R. Liu, Video SAR high-speed processing technology based on FPGA, in Proc. of 2019 IEEE MTT-S Int. Microwave Biomedical Conf. (IMBioC), Nanjing, China, 2019, pp. 1-4.
[28]
F. Zuo and J. Li, A ViSAR imaging method for terahertz band using chirp Z-transform, Communications, Signal Processing, and Systems, , 2020.
[29]
J. Liang, H. F. Zhang, Y. Zhao, G. Chen, Q. Q. Wang, and H. Liu, Study on pointing accuracy effect on image quality of space-borne video SAR, IOP Conf. Ser.: Mater. Sci. Eng., , 2019.
[30]
R. Z. Hu, X. L. Li, T. S. Yeo, Y. Yang, C. Chi, F. Zuo, X. Y. Hu, and Y. M. Pi, Refocusing and zoom-in polar format algorithm for curvilinear spotlight SAR imaging on arbitrary region of interest, IEEE Trans. Geosci. Remote Sens., vol. 57, no. 10, pp. 7995-8010, 2019.
[31]
F. Zuo, R. Min, Y. M. Pi, J. Li, and R. Z. Hu, Improved method of video synthetic aperture radar imaging algorithm, IEEE Geosci. Remote Sens. Lett., vol. 16, no. 6, pp. 897-901, 2019.
[32]
R. Linnehan, J. Miller, and A. Asadi, Map-drift autofocus and scene stabilization for video-SAR, in Proc. of 2018 IEEE Radar Conf. (RadarConf18), Oklahoma City, OK, USA, 2018, pp. 1401-1405.
[33]
M. R. Khosravi, ACI: A bar chart index for non-linear visualization of data embedding and aggregation capacity in IoMT multi-source compression, Wireless Networks, , 2021,
[34]
R. Z. Hu, R. Min, F. Zuo, and Y. M. Pi, An algorithm for persistent imaging of curvilinear video SAR, in Proc. of 2018 IEEE Radar Conf. (RadarConf18), Oklahoma City, OK, USA, 2018, pp. 23-28.
[35]
J. S. Ding, Z. Xu, T. H. Wang, and M. D. Xing, Efficient Doppler ambiguity resolver for video SAR, Electron. Lett., vol. 54, no. 7, pp. 443-445, 2018.
[36]
Z. H. Li, Z. Dong, A. X. Yu, Z. H. He, and X. X. Zhu, A robust image sequence registration algorithm for VideoSAR combining surf with inter-frame processing, in Proc. of IGARSS 2019-2019 IEEE Int. Geoscience and Remote Sensing Symp., Yokohama, Japan, 2019, pp. 2794-2797.
[37]
S. Kim, SAR video generation of MIMO video SAR with beat frequency division FMCW, in Proc. of 2017 11th Int. Conf. Signal Proc. Communication Systems (ICSPCS), Surfers Paradise, Australia, 2017, pp. 1-4.
[38]
S. Kim, J. Yu, S. Y. Jeon, A. Dewantari, and M. H. Ka, Signal processing for a multiple-input, multiple-output (MIMO) video synthetic aperture radar (SAR) with beat frequency division frequency-modulated continuous wave (FMCW), Remote Sens., vol. 9, no. 5, p. 491, 2017.
[39]
C. F. Gu, W. G. Chang, X. Y. Li, and X. Q. Luan, The formation of high-resolution FMCW SAR video, in Proc. of 2016 Progress in Electromagnetic Research Symp. (PIERS), Shanghai, China, 2016, pp. 496-499.
[40]
Y. Zhang, D. Y. Zhu, H. Bi, G. Zhang, and H. Leung, Scattering key-frame extraction for comprehensive VideoSAR summarization: A spatiotemporal background subtraction perspective, IEEE Trans. Instrum. Meas., vol. 69, no. 7, pp. 4768-4784, 2020.
[41]
Z. H. Li, Z. Dong, A. X. Yu, Z. H. He, and T. Z. Yi, An enhanced V-BM3D algorithm for VideoSAR denoising combined with temporal information, in Proc. of 2019 IEEE 4th Int. Conf. Signal and Image Proc., Wuxi, China, 2019, pp. 994-998.
[42]
Z. K. Liu, D. X. An, and X. T. Huang, Moving target shadow detection and global background reconstruction for VideoSAR based on single-frame imagery, IEEE Access, vol. 7, pp. 42418-42425, 2019.
[43]
L. Liao and D. Y. Zhu, An approach for detecting moving target in VideoSAR imagery sequence, in Proc. of 2016 CIE Int. Conf. Radar (RADAR), Guangzhou, China, 2016, pp. 1-3.
[44]
H. Wang, Z. S. Chen, and S. C. Zheng, Preliminary research of low-RCS moving target detection based on Ka-band video SAR, IEEE Geosci. Remote Sens. Lett., vol. 14, no. 6, pp. 811-815, 2017.
[45]
Y. Zhang, S. Y. Yang, H. B. Li, and Z. H. Xu, Shadow tracking of moving target based on CNN for video SAR system, in Proc. of IGARSS 2018-2018 IEEE Int. Geoscience and Remote Sensing Symp., Valencia, Spain, 2018, pp. 4399-4402.
[46]
A. Damini, V. Mantle, and G. Davidson, A new approach to coherent change detection in VideoSAR imagery using stack averaged coherence, in Proc. of 2013 IEEE Radar Conf. (RadarCon13), Ottawa, Canada, 2013, pp. 1-5.
[47]
Z. H. Li, A. X. Yu, Z. Dong, Z. H. He, and T. Z. Yi, Suppressing false alarm in VideoSAR via gradient-weighted edge information, Remote Sens., vol. 11, no. 22, p. 2677, 2019.
[48]
J. S. Ding, L. W. Wen, C. Zhong, and O. Loffeld, Video SAR moving target indication using deep neural network, IEEE Trans. Geosci. Remote Sens., vol. 58, no. 10, pp. 7194-7204, 2020.
[49]
X. J. Huang, J. S. Ding, and Q. H. Guo, Unsupervised image registration for video SAR, IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., vol. 14, pp. 1075-1083, 2021.
[50]
X. Q. Tian, J. Liu, M. Mallick, and K. Y. Huang, Simultaneous detection and tracking of moving-target shadows in ViSAR imagery, IEEE Trans. Geosci. Remote Sens., vol. 59, vol. 2, pp. 1182-1199, 2021.
[51]
F. Gu, H. Zhang, C. Wang, and F. Wu, SAR image super-resolution based on noise-free generative adversarial network, in Proc. of 2019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan, 2019, pp. 2575-2578.
[52]
L. Wang, X. Xu, Y. Yu, R. Yang, R. Gui, Z. Z. Xu, and F. L. Pu, SAR-to-optical image translation using supervised cycle-consistent adversarial networks, IEEE Access, vol. 7, pp. 129136-129149, 2019.
[53]
S. Cohen, T. Gluck, Y. Elovici, and A. Shabtai, Security analysis of radar systems, in Proc. of ACM Workshop on Cyber-Physical Systems Security & Privacy, New York, NY, USA, 2019, pp. 3-14.
[54]
B. W. Liu, X. L. Xu, L. Y. Qi, Q. Ni, and W. C. Dou, Task scheduling with precedence and placement constraints for resource utilization improvement in multi-user MEC environment, J. Syst. Architect., vol. 114, p. 101970, 2021.
[55]
H. Z. Kou, H. W. Liu, Y. C. Duan, W. W. Gong, Y. W. Xu, X. L. Xu, and L. Y. Qi, Building trust/distrust relationships on signed social service network through privacy-aware link prediction process, Appl. Soft Comput., vol. 100, p. 106942, 2021.
[56]
A. A. A. Solyman, H. H. Attar, M. R. Khosravi, V. G. Menon, A. Jolfaei, V. Balasubramanian, B. Selvaraj, and P. Tavallali, A low-complexity equalizer for video broadcasting in cyber-physical social systems through handheld mobile devices, IEEE Access, vol. 8, pp. 67591-67602, 2020.
[57]
H. H. Attar, A. A. A. Solyman, A. E. F. Mohamed, M. R. Khosravi, V. G. Menon, A. K. Bashir, and P. Tavallali, Efficient equalisers for OFDM and DFrFT-OCDM multicarrier systems in mobile E-health video broadcasting with machine learning perspectives, Phys. Commun., vol. 42, p. 101173, 2020.
[58]
H. H. Attar, A. A. A. Solyman, M. R. Khosravi, L. Y. Qi, M. Alhihi, and P. Tavallali, Bit and packet error rate evaluations for half-cycle stage cooperation on 6G wireless networks, Phys. Commun., vol. 44, p. 101249, 2021.
[59]
E. Kalender, G. Guerer, and E. Anadol, Security applications with synthetic aperture radar (SAR) systems, in Proc. of EUSAR 2018; 12th European Conf. Synthetic Aperture Radar, Aachen, Germany, 2018, pp. 1-5.
[60]
Y. Zhang, D. Y. Zhu, X. H. Mao, X. Yu, J. D. Zhang, and Y. Li, Multirotors video synthetic aperture radar: System development and signal processing, IEEE Aerosp. Electron. Syst. Mag., vol. 35, no. 12, pp. 32-43, 2020.
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Received: 13 December 2020
Revised: 06 February 2021
Accepted: 17 February 2021
Published:
29 September 2021
Issue date: April 2022
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The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
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