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Progress In Electromagnetics Research C
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MOTION COMPENSATION ALGORITHM FOR SINGLE TRACK FMCW CSAR BY PARAMETRIC SPARSE REPRESENTATION

By D. Song, B. Li, Y. Qu, and Y. Chen

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Abstract:
In recent years, FMCW CSAR (frequency modulation continue wave circular synthetic aperture radar) is more and more widely used in military reconnaissance and sea surface target recognition. However, due to the influence of external factors, it cannot move in an ideal uniform circular trajectory, resulting in low imaging resolution. In this paper, the problem of motion errors caused by nonuniform circular motion is analyzed, and the phenomenon of range unit broadening and sidelobe increase caused by nonuniform circular motion errors is simulated. The echo model is characterized by error parameters. Based on the compressed sensing imaging algorithm, motion error parameters are estimated by parametric sparse representation. The least squares method and gradient descent method are applied to estimate motion error parameters. Simulations are conducted to show that both of the methods can reach the goal that the motion compensation is realized. The result of simulations and measurement data demonstrate that the algorithm can correct nonuniform circular motion errors better and further improve the imaging resolution.

Citation:
D. Song, B. Li, Y. Qu, and Y. Chen, "Motion Compensation Algorithm for Single Track FMCW CSAR by Parametric Sparse Representation," Progress In Electromagnetics Research C, Vol. 95, 265-279, 2019.
doi:10.2528/PIERC19061002

References:
1. Cumming, I. G. and F. H. Wong, Digital Processing of Synthetic Aperture Radar Data: Algorithms and Implementation, Artech House, Norwood, MA, USA, 2005.

2. Tang, S., et al., "Processing of monostatic SAR data with general configurations," IEEE Trans. Geosci. Remote Sens., Vol. 12, No. 53, 6529-6546, 2015.
doi:10.1109/TGRS.2015.2443835

3. Prats-Iraola, P., et al., "On the processing of very high resolution spaceborne SAR data," Trans. Geosci. Remote Sens., Vol. 10, No. 52, 6003-6016, 2014.
doi:10.1109/TGRS.2013.2294353

4. Lopez-Dekker, P., M. Rodriguez-Cassola, F. De Zan, G. Krieger, and A. Moreira, "Correlating synthetic aperture radar (CoSAR)," IEEE Trans. Geosci. Remote Sens., Vol. 4, No. 54, 2268-2284, 2016.
doi:10.1109/TGRS.2015.2498707

5. Moreira, A., P. Prats-Iraola, M. Younis, G. Krieger, I. Hajnsek, and K. P. Papathanassiou, "A tutorial on synthetic aperture radar," IEEE Geosci. Remote Sens., Vol. 3, No. 1, 6-43, 2013.
doi:10.1109/MGRS.2013.2248301

6. Ciuonzo, D., G. Romano, and R. Solimene, "Performance analysis of time-reversal MUSIC," IEEE Transactions on Signal Processing, Vol. 63, No. 10, 2650-2662, 2015.
doi:10.1109/TSP.2015.2417507

7. Ciuonzo, D., "On time-reversal imaging by statistical testing," IEEE Transactions on Signal Processing Letters, Vol. 24, No. 4, 1024-1028, 2017.
doi:10.1109/LSP.2017.2704612

8. Ciuonzo, D., V. Carotenuto, and A. De Maio, "On multiple covariance equality testing with application to SAR change detection," IEEE Transactions on Signal Processing, Vol. 65, No. 19, 5078-5091, 2017.
doi:10.1109/TSP.2017.2712124

9. Soumekh, M., "Reconnaissance with slant plane circular SAR imaging," IEEE Transactions on Image Processing, Vol. 5, No. 8, 1252-1265, 1996.
doi:10.1109/83.506760

10. Soumekh, M., Synthetic Aperture Radar Signal Processing with MATLAB Algorithms, Wiley, New York, 1999.

11. Ao, D., R. Wang, C. Hu, and Y. Li, "A sparse SAR imaging method based on multiple measurement vectors model," Remote Sens., Vol. 9, No. 297, 1-22, 2017.

12. Jia, G., W. Chang, Q. Zhang, and X. Luan, "The analysis and realization of motion compensation for circular synthetic aperture radar data," IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 9, No. 4, 3060-3071, 2016.
doi:10.1109/JSTARS.2016.2553051

13. Guo, Z. Y., Y. Lin, W. X. Tan, Y. P. Wang, and W. Hong, "Circular SAR motion compensation using trilateration and phase correction," IET International Radar Conference, 1-6, 2013.

14. Xie, H., et al., "Fast factorized backprojection algorithm for one-stationary bistatic spotlight circular SAR image formation," IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 10, No. 4, 1494-1510, 2017.
doi:10.1109/JSTARS.2016.2639580

15. Zhang, B., X. Zhang, and S. Wei, "A circular SAR image autofocus algorithm based on minimum entropy," 2015 IEEE 5th Asia-Paci¯c Conference on Synthetic Aperture Radar (APSAR), 152-155, 2015.
doi:10.1109/APSAR.2015.7306177

16. Lin, Y., W. Hong, and W. Tan, "Compressed sensing technique for circular SAR imaging," 2009 IET International Radar Conference, 1-4, 2009.

17. Wang, X., B. Deng, H. Wang, and Y. Qi, "Ground moving target imaging based on motion compensation for circular SAR," 2017 9th International Conference on Advanced Infocomm Technology, 372-377, 2017.
doi:10.1109/ICAIT.2017.8388948

18. Chen, Y.-C., G. Li, Q. Zhang, Q.-J. Zhang, and X.-G. Xia, "Motion compensation for airborne SAR via parametric sparse representation," IEEE Trans. Geosci. Remote Sens., Vol. 55, No. 1, 551-561, 2017.
doi:10.1109/TGRS.2016.2611522

19. Rao, W., G. Li, X. Wang, and X.-G. Xia, "Parametric sparse representation method for ISAR imaging of rotating targets," IEEE Trans. Aerosp. Electron. Syst., Vol. 2, No. 50, 910-919, 2014.
doi:10.1109/TAES.2014.120535

20. Li, X., S. Liu, and W. Xie, "A novel conjugate gradient method for sensing matrix optimization for compressed sensing systems," Journal of Zhejiang University (Science Edition), Vol. 46, No. 1, 15-21, 2019.


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