volume | PIER Journals

Abstract

Microwave staring correlated imaging (MSCI) is a super-resolution imaging technique based on temporal-spatial stochastic radiation fields (TSSRFs), which requires an accurate calculation of the electromagnetic field at the imaging plane. However, systematic errors always exist in practice, such as the time synchronization and frequency synchronization errors of radar systems, which make it difficult to calculate the required TSSRFs accurately, and this deteriorates the imaging results. Meanwhile, some imaging algorithms have problems such as high computational complexity. In this paper, an intelligent MSCI method based on the deep neural network (DNN) is proposed, which can accomplish imaging directly from the echoes, avoiding the computation of TSSRFs. A multi-level residual convolutional neural network (MRCNN) is developed for the DNN, and simulations and experiments are carried out to obtain the dataset for training and testing the MRCNN. Compared with the conventional MSCI methods, the imaging results verify the effectiveness of intelligent MSCI in terms of imaging quality and computational efficiency.

1. Dunkel, R., R. Saddler, and A. Doerry, "Synthetic aperture radar for disaster monitoring," Radar Sensor Technology XV, 125-134, 2011. Google Scholar

2. Madsen, S., W. Edelstein, L. D. DiDomenico, and J. LaBrecque, "A geosynchronous synthetic aperture radar; for tectonic mapping, disaster management and measurements of vegetation and soil moisture," IEEE 2001 International Geoscience and Remote Sensing Symposium (IGARSS), Vol. 1, 447-449, 2001. Google Scholar

3. Deng, B., Y. Qin, Y. Li, H. Wang, and X. Li, "A novel approach to range doppler SAR processing based on legendre orthogonal polynomials," IEEE Geoscience and Remote Sensing Letters, Vol. 6, No. 1, 13-17, 2008.
doi:10.1109/LGRS.2008.2006572 Google Scholar

4. Zhu, Y., Y. Su, and W. Yu, "An ISAR imaging method based on MIMO technique," IEEE Transactions on Geoscience and Remote Sensing, Vol. 48, No. 8, 3290-3299, 2010.
doi:10.1109/TGRS.2010.2045230 Google Scholar

5. Guo, Y., D. Wang, X. He, and B. Liu, "Super-resolution staring imaging radar based on stochastic radiation fields," 2012 IEEE MTT-S International Microwave Workshop Series on Millimeter Wave Wireless Technology and Applications, 1-4, 2012. Google Scholar

6. Li, D., X. Li, Y. Qin, Y. Cheng, and H. Wang, "Radar coincidence imaging: An instantaneous imaging technique with stochastic signals," IEEE Transactions on Geoscience and Remote Sensing, Vol. 52, No. 4, 2261-2277, 2013. Google Scholar

7. Xu, X., X. Zhou, Y. Cheng, and Y. Qin, "Radar coincidence imaging with array position error," 2015 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC), 1-4, 2015. Google Scholar

8. Zhou, X., H. Wang, Y. Cheng, and Y. Qin, "Sparse auto-calibration for radar coincidence imaging with gain-phase errors," Multidisciplinary Digital Publishing Institute, Vol. 15, No. 11, 27611-27624, 2015. Google Scholar

9. Zhou, X., H. Wang, and Y. Cheng, "Expansion-compression variance-component-based autofocusing method for joint radar coincidence imaging and gain-phase error calibration," SPIE, Vol. 11, No. 2, 025002, 2017. Google Scholar

10. Cao, K., X. Zhou, Y. Cheng, and Y. Qin, "Improved focal underdetermined system solver method for radar coincidence imaging with model mismatch," Journal of Electronic Imaging, Vol. 26, No. 3, 033001, 2017.
doi:10.1117/1.JEI.26.3.033001 Google Scholar

11. Cao, K., X. Zhou, Y. Cheng, B. Fan, and Y. Qin, "Total variation-based method for radar coincidence imaging with model mismatch for extended target," Journal of Electronic Imaging, Vol. 26, No. 6, 063007, 2017.
doi:10.1117/1.JEI.26.6.063007 Google Scholar

12. He, X., B. Liu, S. Chai, and D. Wang, "A novel approach of high spatial-resolution microwave staring imaging," Conference Proceedings of 2013 Asia-Pacific Conference on Synthetic Aperture Radar (APSAR), 75-78, 2013. Google Scholar

13. Meng, Q., B. Liu, C. Tian, Y. Guo, and D. Wang, "Correlation algorithm of microwave staring correlated imaging based on multigrid and CGLS," 2015 IEEE International Conference on Communication Problem-Solving (ICCP), 359-362, 2015. Google Scholar

14. Yu, H., G. H. Lu, and H. L. Zhang, "Adaptive sparse recovery of moving targets for distributed MIMO radar," Advanced Materials Research, Vol. 933, 450-455, Trans. Tech. Publ., 2014. Google Scholar

15. Lou, G. and H. Shi, "Face image recognition based on convolutional neural network," China Communications, Vol. 17, No. 2, 117-124, 2020.
doi:10.23919/JCC.2020.02.010 Google Scholar

16. Shen, W. and W. Wang, "Node identification in wireless network based on convolutional neural network," 2018 14th International Conference on Computational Intelligence and Security (CIS), 238-241, 2018. Google Scholar

17. Goodfellow, I., J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, "Generative adversarial nets," Advances in Neural Information Processing Systems, Vol. 27, 2014. Google Scholar

18. Ledig, C., L. Theis, F. Huszar, J. Caballero, A. Cunningham, A. Acosta, A. Aitken, A. Tejani, J. Totz, Z.Wang, et al. "Photo-realistic single image super-resolution using a generative adversarial network," Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 4681-4690, 2017. Google Scholar

19. Long, J., E. Shelhamer, and T. Darrell, "Fully convolutional networks for semantic segmentation," Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 3431-3440, 2015. Google Scholar

20. Girshick, R., J. Donahue, T. Darrell, and J. Malik, "Rich feature hierarchies for accurate object detection and semantic segmentation," Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 580-587, 2014. Google Scholar

21. Milletari, F., N. Navab, and S.-A. Ahmadi, "V-net: Fully convolutional neural networks for volumetric medical image segmentation," 2016 Fourth International Conference on 3D Vision (3DV), 565-571, 2016.
doi:10.1109/3DV.2016.79 Google Scholar

22. Niu, S., X. Qiu, L. Peng, and B. Lei, "Parameter prediction method of SAR target simulation based on convolutional neural networks," EUSAR 2018; 12th European Conference on Synthetic Aperture Radar, 1-5, 2018. Google Scholar

23. Shao, W. and Y. Du, "Microwave imaging by deep learning network: Feasibility and training method," IEEE Transactions on Antennas and Propagation, Vol. 68, No. 7, 5626-5635, 2020.
doi:10.1109/TAP.2020.2978952 Google Scholar

24. Li, L., Y. Shuang, Q. Ma, H. Li, H. Zhao, M. Wei, C. Liu, C. Hao, C.-W. Qiu, and T. J. Cui, "Intelligent metasurface imager and recognizer," Light: Science & Applications, Vol. 8, No. 1, 1-9, 2019.
doi:10.1038/s41377-018-0109-7 Google Scholar

25. Li, H.-Y., H.-T. Zhao, M.-L. Wei, H.-X. Ruan, Y. Shuang, T. J. Cui, P. Del Hougne, and L. Li, "Intelligent electromagnetic sensing with learnable data acquisition and processing," Patterns, Vol. 1, No. 1, 100006, 2020.
doi:10.1016/j.patter.2020.100006 Google Scholar

26. Xing, Y., L. Zhong, and X. Zhong, "An encoder-decoder network based FCN architecture for semantic segmentation," Wireless Communications and Mobile Computing, Vol. 2020, 2020. Google Scholar

27. Deng, L., "The mnist database of handwritten digit images for machine learning research [best of the web]," IEEE Signal Processing Magazine, Vol. 29, No. 6, 141-142, 2012.
doi:10.1109/MSP.2012.2211477 Google Scholar

28. He, K., X. Zhang, S. Ren, and J. Sun, "Deep residual learning for image recognition," Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 770-778, 2016. Google Scholar

29. Hunt, J., T. Driscoll, A. Mrozack, G. Lipworth, M. Reynolds, D. Brady, and D. R. Smith, "Metamaterial apertures for computational imaging," Science, Vol. 339, No. 6117, 310-313, 2013.
doi:10.1126/science.1230054 Google Scholar

30. Sleasman, T., M. Boyarsky, M. F. Imani, J. N. Gollub, and D. R. Smith, "Design considerations for a dynamic metamaterial aperture for computational imaging at microwave frequencies," JOSA B, Vol. 33, No. 6, 1098-1111, 2016.
doi:10.1364/JOSAB.33.001098 Google Scholar

31. Imani, M. F., T. Sleasman, and D. R. Smith, "Two-dimensional dynamic metasurface apertures for computational microwave imaging," IEEE Antennas and Wireless Propagation Letters, Vol. 17, No. 12, 2299-2303, 2018.
doi:10.1109/LAWP.2018.2873131 Google Scholar

32. Hoang, T. V., V. Fusco, T. Fromenteze, and O. Yurduseven, "Computational polarimetric imaging using two-dimensional dynamic metasurface apertures," IEEE Open Journal of Antennas and Propagation, Vol. 2, 488-497, 2021.
doi:10.1109/OJAP.2021.3069320 Google Scholar

33. Sleasman, T. A., M. F. Imani, A. V. Diebold, M. Boyarsky, K. P. Trofatter, and D. R. Smith, "Implementation and characterization of a two-dimensional printed circuit dynamic metasurface aperture for computational microwave imaging," IEEE Transactions on Antennas and Propagation, Vol. 69, No. 4, 2151-2164, 2020.
doi:10.1109/TAP.2020.3027188 Google Scholar

Dr. Kui Ying

Dr. Xinyu Yu

Dr. Jiana Shen

Mr. Shilu Zhang

Dr. Yuanyue Guo