Progress In Electromagnetics Research M
ISSN: 1937-8726
Home | Search | Notification | Authors | Submission | PIERS Home | EM Academy
Home > Vol. 46 > pp. 1-10


By Y. Zhang, Y. Huang, Y. Zha, and J. Yang

Full Article PDF (391 KB)

A maximum a posteriori (MAP) approach, based on the Bayesian criterion, is proposed to overcome the low cross-range resolution problem in forward-looking imaging. We adapt scanning radar system to record received data and exploit deconvolution method to enhance the real-aperture resolution because the received echo is the convolution of target scattering coefficient and antenna pattern. The Generalized Gaussian distribution is considered as the prior information of target scattering coefficient in MAP approach for the reason that it could express different target scattering coefficient properties with the control of statistic parameter. This constraint term makes the proposed algorithm useful in different applications. On the other hand, the reconstruction problem can also be viewed as the lp-norm (0 < p ≤ 2) regularization. Simulation results show the robustness of the proposed algorithm against additive noise compared with other superresolution methods.

Y. Zhang, Y. Huang, Y. Zha, and J. Yang, "Superresolution Imaging for Forward-Looking Scanning Radar with Generalized Gaussian Constraint," Progress In Electromagnetics Research M, Vol. 46, 1-10, 2016.

1. Schiavulli, D., F. Nunziata, G. Pugliano, and M. Migliaccio, "Reconstruction of the normalized radar cross section field from gnss-r delay-doppler map," IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 7, No. 5, 1573-1583, 2014.

2. Ausherman, D. A., A. Kozma, J. L. Walker, H. M. Jones, and E. C. Poggio, "Developments in radar imaging," IEEE Transactions on Aerospace and Electronic Systems, Vol. 4, No. AES-20, 363-400, 1984.

3. Curlander, J. C. and R. N. McDonough, Synthetic Aperture Radar, John Wiley & Sons, 1991.

4. Jakowatz, C. V., D. E. Wahl, P. H. Eichel, D. C. Ghiglia, and P. A. Thompson, Spotlight-mode Synthetic Aperture Radar: A Signal Processing Approach, Springer Science & Business Media, 2012.

5. Loehner, A., "Improved azimuthal resolution of forward looking sar by sophisticated antenna illumination function design," IEE Proceedings - Radar, Sonar and Navigation, Vol. 145, No. 2, 128-134, 1998.

6. Dai, S. and W. Wiesbeck, "High resolution imaging for forward looking sar with multiple receiving antennas," IEEE 2000 International Geoscience and Remote Sensing Symposium, 2000. Proceedings. IGARSS 2000, Vol. 5, 2254-2256, IEEE, 2000.

7. Espeter, T., I. Walterscheid, J. Klare, A. R. Brenner, and J. H. Ender, "Bistatic forward-looking sar: results of a spaceborne-airborne experiment," IEEE Geoscience and Remote Sensing Letters, Vol. 8, No. 4, 765-768, 2011.

8. Wu, J., Z. Li, Y. Huang, J. Yang, H. Yang, and Q. H. Liu, "Focusing bistatic forward-looking sar with stationary transmitter based on keystone transform and nonlinear chirp scaling," IEEE Geoscience and Remote Sensing Letters, Vol. 11, No. 1, 148-152, 2014.

9. Högbom, J., "Aperture synthesis with a non-regular distribution of interferometer baselines," Astronomy and Astrophysics Supplement Series, Vol. 15, 417, 1974.

10. Clark, B., "An efficient implementation of the algorithm `clean'," Astronomy and Astrophysics, Vol. 89, 377, 1980.

11. Bose, R., A. Freedman, and B. D. Steinberg, "Sequence clean: A modified deconvolution technique for microwave images of contiguous targets," IEEE Transactions on Aerospace and Electronic Systems, Vol. 38, No. 1, 89-97, 2002.

12. Bayliss, E. T., "Design of monopulse antenna difference patterns with low sidelobes," Bell System Technical Journal, Vol. 47, No. 5, 623-650, 1968.

13. Sherman, S. M. and D. K. Barton, Monopulse Principles and Techniques, Artech House, 2011.

14. Caorsi, S., A. Massa, M. Pastorino, and A. Randazzo, "Optimization of the difference patterns for monopulse antennas by a hybrid real/integer-coded differential evolution method," IEEE Transactions on Antennas and Propagation, Vol. 53, No. 1, 372-376, 2005.

15. Pérez-Martínez, F., J. Garcia-Fominaya, and J. Calvo-Gallego, "A shift-and-convolution technique for high-resolution radar images," IEEE Sensors Journal, Vol. 5, No. 5, 1090-1098, 2005.

16. Munoz-Ferreras, J., J. Calvo-Gallego, F. Pérez-Martínez, A. Blanco-del Campo, A. Asensio-Lopez, and B. Dorta-Naranjo, "Motion compensation for isar based on the shift-and-convolution algorithm," 2006 IEEE Conference on Radar, Vol. 5, IEEE, 2006.

17. Ly, C., H. Dropkin, and A. Z. Manitius, "Extension of the music algorithm to millimeter-wave (mmw) real-beam radar scanning antennas," AeroSense 2002, 96-107, International Society for Optics and Photonics, 2002.

18. Uttam, S. and N. A. Goodman, "Superresolution of coherent sources in real-beam data," IEEE Transactions on Aerospace and Electronic Systems, Vol. 46, No. 3, 1557-1566, 2010.

19. Zhang, Y., Y. Zhang, Y. Huang, J. Yang, Y. Zha, J. Wu, and H. Yang, "Ml iterative superresolution approach for real-beam radar," 2014 IEEE Radar Conference, 1192-1196, IEEE, 2014.

20. Zhang, Y., Y. Zhang, W. Li, Y. Huang, and J. Yang, "Angular superresolution for real beam radar with iterative adaptive approach," 2013 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 3100-3103, IEEE, 2013.

21. Zhang, Y., W. Li, Y. Zhang, Y. Huang, and J. Yang, "A fast iterative adaptive approach for scanning radar angular superresolution," IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. PP, No. 99, 1-10, 2015.

22. Tan, X., W. Roberts, J. Li, and P. Stoica, "Sparse learning via iterative minimization with application to mimo radar imaging," IEEE Transactions on Signal Processing, Vol. 59, No. 3, 1088-1101, 2011.

23. Xu, G., M. Xing, L. Zhang, Y. Liu, and Y. Li, "Bayesian inverse synthetic aperture radar imaging," IEEE Geoscience and Remote Sensing Letters, Vol. 8, No. 6, 1150-1154, 2011.

24. Guan, J., J. Yang, Y. Huang, and W. Li, "Maximum a posteriori based angular superresolution for scanning radar imaging," IEEE Transactions on Aerospace and Electronic Systems, Vol. 50, No. 3, 2389-2398, 2014.

25. Zha, Y., Y. Huang, Z. Sun, Y. Wang, and J. Yang, "Bayesian deconvolution for angular super-resolution in forward-looking scanning radar," Sensors, Vol. 15, No. 3, 6924-6946, 2015.

26. Osher, S., M. Burger, D. Goldfarb, J. Xu, and W. Yin, "An iterative regularization method for total variation-based image restoration," Multiscale Modeling & Simulation, Vol. 4, No. 2, 460-489, 2005.

27. Golub, G. H., P. C. Hansen, and D. P. O’Leary, "Tikhonov regularization and total least squares," SIAM Journal on Matrix Analysis and Applications, Vol. 21, No. 1, 185-194, 1999.

28. Vauhkonen, M., D. Vadasz, P. A. Karjalainen, E. Somersalo, and J. P. Kaipio, "Tikhonov regularization and prior information in electrical impedance tomography," IEEE Transactions on Medical Imaging, Vol. 17, No. 2, 285-293, 1998.

29. Aster, R. C., B. Borchers, and C. H. Thurber, Parameter Estimation and Inverse Problems, Academic Press, 2011.

© Copyright 2010 EMW Publishing. All Rights Reserved