SAR Ground  Moving Target Imaging based on Sparse Representation

Moezzi, S. Andishe; Masnadi-Shirazi, Mohamad Ali

doi:10.22034/jsst.2021.1272

SAR Ground Moving Target Imaging based on Sparse Representation

Document Type : Original Research Paper

Authors

S. Andishe Moezzi ¹

Mohamad Ali Masnadi-Shirazi ²

¹ Department of Electrical and Computer Engineering , shiraz University, shiraz, Iran

² Professor, Department of Electrical and Computer Engineering, Shiraz University, Shiraz, Iran

10.22034/jsst.2021.1272

Abstract

synthetic aperture radar (SAR) for ground moving target indication (GMTI) and imaging (GMTIm) have been gaining increasing interests for both civilian and military applications. Because SAR is generally designed for imaging a stationary scene, the SAR image of a moving target will be both displaced and smeared.
More specifically, by exploiting the inherent sparsity of the moving targets in the clutter-suppressed SAR image domain, in this article. the intended SAR-GMTIm problem is solve by a sparse Bayesian perspective.
The theory of CS has been successfully applied to SAR/ISAR imagery to achieve high cross-range resolution with a limited number of pulses
In order to evaluate the quality of images, we apply the target-to-clutter ratio (TCR), which is commonly used in synthetic
aperture radar (SAR) image assessment.
The proposed algorithm shows a 10-dB higher TCR compared to the conventional algorithm.

Keywords

: Synthetic Aperture Radar (SAR)

Ground Moving Target Indication (GMTI)

Ground Moving Target Imaging (GMTIm)

Sparse Bayesian Learning(SBL)

Lv’s Distribution (LVD)

VB-EM

20.1001.1.20084560.1400.14.3.8.9

Subjects

Remote sensing

Article Title Persian

تصویر‌برداری از هدف زمینی در حال حرکت توسط رادار دهانه مصنوعی مبتنی بر نمایش تنک

Authors Persian

سیده اندیشه معزی ¹

محمدعلی مسندی شیرازی ²

¹ دانشکده مهندسی برق و کامپیوتر ,دانشگاه شیراز,شیراز,ایران

² استاد دانشکده مهندسی برق و کامپیوتر،دانشگاه شیراز،شیراز،ایران

Abstract Persian

امروزه علاقه روز‌‌افزونی مبنی بر استفاده از رادار دهانه مصنوعی(SAR) در کاربرد آشکارسازی اهداف متحرک زمینی (GMTI ) و تصویر‌برداری از اهداف متحرک زمینی ( (GMTIm برای هر دو کاربرد نظامی و غیر نظامی وجود دارد.از آنجا که SAR برای تصویربرداری از صحنه ثابت طراحی شده است، تصویر SAR از هدف در حال حرکت مات و جابه‌جا می‌شود. از این‌رو برای به دست آوردن تصویر با وضوح بالا در این مقاله از یک الگوریتم جدید استفاده شده است که چارچوب آن مبتنی بر یادگیری بیزی تنک (SBL) است. برای ارزیابی کیفیت تصاویر، از نسبت هدف به کلاتر (TCR) و آنتروپی شانون استفاده شده است که معمولاً برای ارزیابی تصویر رادار دهانه مصنوعی استفاده می‌شود. الگوریتم پیشنهادی TCR تصویر را در مقایسه با روش‌های متداول در حدود 10dB افزایش و آنتروپی تصویر را به مقدار 60%کاهش می‌دهد.

Keywords Persian

رادار دهانه مصنوعی(SAR)

آشکارسازی اهداف متحرک زمینی(GMTI)

تصویربرداری از اهداف متحرک زمینی(GMTIm )

یادگیری بیزی تنک(SBL)

توزیعLV

روش VB-EM

1. Mensa, High Resolution Radar Imaging, Norwood, MA, USA:Artech House, 1981.

2. Ian, F. Cumming and H. Wong, Digital Processing of Synthetic Aperture Radar Data: Algorithms and Implementation, Artech House: London, 2005.

3. Candes and T. Tao, “Decoding by linear programming,” IEEE Trans. Inf. Theory, vol. 51, no. 12, pp. 4203-4215, 2005.

4. Cetin, “Feature –enhanced synthetic aperture radar imaging, PhD dissertation, College of Engineering, Boston University, Boston, MA, USA, 2001.

5. Ji, Y. Xue and L. Carin, “Bayesian compressive sensing,” IEEE Trans. Signal Process, vol. 56, no. 6, pp. 2346–2356, 2008.

6. D. Babacan, R. Molina and A.K. Katsaggelos, “Bayesian compressive sensing using laplace priors,” IEEE Trans. Image Process, vol. 19, no. 1, pp. 53–63, 2010.

7. G. Tzikas, C.L. Likas and N.P. Galatsanos, “The variational approximation for bayesian inference,” IEEE Signal Process. Mag. Vol. 25, no. 6, pp. 131–146, 2008.

8. K. Moon, “The expectation-maximization algorithm,” IEEE Signal Process. Mag, Vol. 13, no. 6, pp. 47–60, 1996.

9. Stojanovic and W.C. Karl, “Imaging of moving targets with multi-static sar using an overcomplete dictionary,” IEEE J. Sel. Topics Signal Process. Vol. 4, no. 1, pp. 164–176, 2010.

10. Ji, D. Dunson, and L. Carin, “Multitask compressive sensing,” IEEE Trans Signal Process. Vol. 57, no. 1, pp. 92–106, 2009.

11. Lv, G. Bi, C. Wan and M. Xing, “Lv’s distribution: Principle, implementation,properties, and performance,” IEEE Trans. Signal Process, vol. 59, no. 8, pp. 3576–3591, 2011.

12. Zhao, L. Wang, G. Bi and L.Yang, “An autofocus technique for highresolution inverse synthetic aperture radar imagery,” IEEE Trans. Geosci.Remote Sens. Vol. 52, no. 10, pp. 6392–6403, 2014.

13. Wang, L. Zhao, G. Bi, C. Wan and L.Yang, “Enhanced isar imaging by exploiting the continuity of the target scene,” IEEE Trans. Geosci. Remote Sens. Vol. 52, no. 9, pp. 5736–5750, 2014.

14. Yang and Y. Zhang, “Analysis on the Azimuth Shift of a Moving Target in SAR Image,” Progress In Electromagnetics Research M, vol. 42, pp. 121–134, 2015.

15. Hlawatsch and G.F. Bourdeaux-Bartels, “Linear and quadratic timefrequency signal representations,” IEEE Signal Process. Mag.vol. 9, no. 2, pp. 21–67, 1992.

16. J. Kragh, “Monotonic iterative algorithm for minimum-entropy autofocus,” in Proc. ASAP Workshop, Jun. 2006, pp. 1–6.

17. Zhang, “Resolution enhancement for inversed synthetic aperture radar imaging under low snr via improved compressive sensing,” IEEE Trans. Geosci. Remote Sens. Vol. 48, no. 10, pp. 3824–3838, 2010.

18. Yang, L. Zhao, G. Bi and L. Zhang, “SAR ground moving target imaging algorithm based on parametric and dynamic sparse Bayesian learning,”IEEE Trans. Geosci. Remote Sens, vol. 54, no. 4, pp. 2254–2267, 2016.

19. Yang, L. Zhao, S. Zhou and G. Bi , “Sparsity-Driven SAR Imaging for Highly Maneuvering Ground Target by the Combination of Time-Frequency Analysis and ParametricBayesian Learning”, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 10, no. 4, pp. 1453–1465, 2017.

20. T. Crockett, “Target Motion Estimation Techniques for single-Channel SAR” (Master’s thesis) Brigham Young University, 2014.

21. Chang, H. Tao, G. Sun, Y. Wang and Z. Bao,"A novel multi-angle sar imaging system and method based on an ultrahigh speed platform", Sensors, vol. 19, no. 1701, pp. 1-17, 2019.

22. Rahmanizadeh, J. Amini," An Integrated Method for Simulation of Synthetic Aperture Radar (SAR) Raw Data in MovingTarget Detection", Remote sensing, vol. 9, no. 1009, pp.1-19, 2017.

23. Chen , Y. Zhou, L. Zhang , C. Lin , Y. Huang and S. Tang, "Ground Moving Target Imaging and Analysis for Near-Space Hypersonic Vehicle-Borne Synthetic Aperture Radar System with Squint Angle", Remote sensing, vol. 10, no. 1966, pp. 1-25, 2018.

24. Huang, G. Liao, Y. Zhang, J. Xu, J. Li , "Effective baseline estimation in dual-channel synthetic aperture radar for moving target imaging and relocation", Digital Signal Processing, vol.78. pp. 121-135, 2018.

25. Li, C. Zhang , H. Ma, H. Liu, J. Su, and Qi. Liu, An Efficient SAR Ground Moving Target Refocusing Method Based on PPFFT and Coherently Integrated CPF, IEEE Access, vol.7, pp. 114102- 114115, 2019.

26. R. Benitz, "High-definition vector imaging", Lincoln Laboratory Journal,Vol.10, no.2, pp.147-170, 1997.

27. Cetin, W.C. Karl, and D.A. Castanon," Feature enhancement and ATR performance using nonquadratic optimization-based SAR imaging", IEEE Transactions on Aerospace and Electronic Systems, vol.39, no.4, pp.1375-1395, 2003.