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MULTISTATIC AIRBORNE PASSIVE SYNTHETIC APERTURE RADAR IMAGING BASED ON TWO-LEVEL BLOCK SPARSITY

By L. Qu and Y. Liu

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Abstract:
Available of multiple illuminators in a multistatic airborne passive synthetic aperture radar (SAR) system can enhance SAR imaging quality. In this paper, a new imaging algorithm based on two-level block sparsity for a multistatic airborne passive SAR system is proposed. The proposed imaging algorithm named by two-level block matching pursuit (BMP) algorithm utilizes both the spatially clustered property of observed targets and joint sparsity of the multistatic observation, i.e. two-level block sparsity to achieve imaging reconstruction of an observed scene. The simulation results show that the proposed two-level BMP imaging algorithm for the multistatic airborne passive SAR system can reduce imaging reconstruction time and provide enhanced imaging reconstruction quality compared to the state-of-the-art structured sparse imaging algorithm.

Citation:
L. Qu and Y. Liu, "Multistatic Airborne Passive Synthetic Aperture Radar Imaging Based on Two-Level Block Sparsity," Progress In Electromagnetics Research M, Vol. 87, 93-102, 2019.
doi:10.2528/PIERM19093003

References:
1. Yonel, B., E. Mason, and B. Yazıcı, "Deep learning for passive synthetic aperture radar," IEEE J. Sel. Topics Signal Process., Vol. 12, No. 1, 90-103, Feb. 2018.
doi:10.1109/JSTSP.2017.2784181

2. Wan, X., J. Yi, Z. Zhao, and H. Ke, "Experimental research for CMMB-based passive radar under a multipath environment," IEEE Trans. Aerosp. Electron. Syst., Vol. 50, No. 1, 70-85, Jan. 2014.
doi:10.1109/TAES.2013.120737

3. Liu, F., M. Antoniou, Z. Zeng, and M. Cherniakov, "Coherent change detection using passive GNSS-based BSAR: experimental proof of concept," IEEE Trans. Geosci. Remote Sens., Vol. 51, No. 8, 4544-4555, Aug. 2013.
doi:10.1109/TGRS.2012.2231082

4. Pastina, D., et al., "Maritime moving target long time integration for GNSS-based passive bistatic radar," IEEE Trans. Aerosp. Electron. Syst., Vol. 54, No. 6, 3060-3083, Dec. 2018.
doi:10.1109/TAES.2018.2840298

5. Tan, D. K. P., M. Lesturgie, H. Sun, and Y. Lu, "Space-time interference analysis and suppression for airborne passive radar using transmissions of opportunity," IET Radar, Sonar and Navigation, Vol. 8, No. 2, 142-152, Feb. 2014.
doi:10.1049/iet-rsn.2013.0190

6. Deng, Y., J. Wang, Z. Luo, and S. Guo, "Cascaded suppression method for airborne passive radar with contaminated reference signal," IEEE Access, Vol. 7, 50317-50329, 2019.
doi:10.1109/ACCESS.2019.2911136

7. Yang, P., X. L. Yu, Z. Chai, D. Zhang, Q. Yue, and J. Yang, "Clutter cancellation along the clutter ridge for airborne passive radar," IEEE Geosci. Remote Sens. Lett., Vol. 14, No. 6, 951-955, Jun. 2017.
doi:10.1109/LGRS.2017.2689076

8. Berthillot, C., A. Santori, O. Rabaste, D. Poullin, and M. Lesturgie, "BEM reference signal estimation for an airborne passive radar antenna array," IEEE Trans. Aerosp. Electron. Syst., Vol. 53, No. 6, 2833-2845, Dec. 2017.
doi:10.1109/TAES.2017.2716458

9. Wang, L., C. E. Yarman, and B. Yazici, "Doppler-Hitchhiker: A novel passive synthetic aperture radar using ultranarrowband sources of opportunity," IEEE Trans. Geosci. Remote Sens., Vol. 49, No. 10, 3521-3537, Oct. 2011.
doi:10.1109/TGRS.2011.2142420

10. Dawidowicz, B., K. S. Kulpa, M. Malanowski, J. Misiurewicz, P. Samczynski, and M. Smolarczyk, "DPCA detection of moving targets in airborne passive radar," IEEE Trans. Aerosp. Electron. Syst., Vol. 48, No. 2, 1347-1357, Apr. 2012.
doi:10.1109/TAES.2012.6178066

11. Gromek, D., K. Kulpa, and P. Samczynski, "Experimental results of passive SAR imaging using DVB-T illuminators of opportunity," IEEE Geosci. Remote Sens. Lett., Vol. 13, No. 8, 1124-1128, Aug. 2016.
doi:10.1109/LGRS.2016.2571901

12. Gromek, D., K. Radecki, J. Drozdowicz, P. Samczynski, and J. Szabatin, "Passive SAR imaging using DVB-T illumination for airborne applications," IET Radar, Sonar and Navigation, Vol. 13, No. 2, 213-221, Feb. 2019.
doi:10.1049/iet-rsn.2018.5123

13. Liu, C. C. and W. D. Chen, "Sparse self-calibration imaging via iterative MAP in FM-based distributed passive radar," IEEE Geosci. Remote Sens. Lett., Vol. 10, No. 3, 538-542, Oct. 2013.
doi:10.1109/LGRS.2012.2212272

14. Qiu, W., et al., "Compressive sensing-based algorithm for passive bistatic ISAR with DVB-T signals," IEEE Trans. Aerosp. Electron. Syst., Vol. 51, No. 3, 2166-2180, Jul. 2015.
doi:10.1109/TAES.2015.130761

15. Yu, X. F., T. Y. Wang, X. F. Lu, C. Chen, and W. D. Chen, "Sparse passive radar imaging based on DVB-S using the Laplace-SLIM algorithm," 2014 International Radar Conference, 1-4, Lille, 2014.

16. Zhang, Y. D., M. G. Amin, and B. Himed, "Structure-aware sparse reconstruction and applications to passive multistatic radar," IEEE Aerosp. Electron. Syst. Mag., Vol. 32, No. 2, 68-78, Feb. 2017.
doi:10.1109/MAES.2017.160021

17. Wu, Q., Y. D. Zhang, M. G. Amin, and B. Himed, "High-resolution passive SAR imaging exploiting structured Bayesian compressive sensing," IEEE J. Sel. Topics Signal Process., Vol. 9, No. 8, 1484-1497, Dec. 2015.
doi:10.1109/JSTSP.2015.2479190

18. Wang, X., G. Li, Y. Liu, and M. G. Amin, "Two-level block matching pursuit for polarimetric through-wall radar imaging," IEEE Trans. Geosci. Remote Sens., Vol. 56, No. 3, 1533-1545, Mar. 2018.
doi:10.1109/TGRS.2017.2764920

19. Cevher, V., P. Indyk, L. Carin, and R. G. Baraniuk, "Sparse signal recovery and acquisition with graphical models," IEEE Signal Process. Mag., Vol. 27, No. 6, 92-103, Nov. 2010.

20. Cevher, V., M. F. Duarte, C. Hegde, and R. G. Baraniuk, "Sparse signal recovery using Markov random fields," Proc. Adv. Neural. Inf., 257-264, 2009.

21. Tropp, J. A., A. C. Gilbert, and M. J. Strauss, "Algorithms for simultaneous sparse approximation. Part I: Greedy pursuit?," Signal Process., Vol. 86, No. 3, 572-588, Mar. 2006.
doi:10.1016/j.sigpro.2005.05.030

22. Koller, D. and N. Friedman, Probabilistic Graphical Models-Principles and Techniques, MIT Press, Cambridge, MA, USA, 2009.

23. Ward, R., "Compressed sensing with cross validation," IEEE Trans. on Inf. Theory, Vol. 55, No. 12, 5773-5782, Dec. 2009.
doi:10.1109/TIT.2009.2032712

24. Zhang, J., L. Chen, P. T. Boufounos, and Y. Gu, "On the theoretical analysis of cross validation in compressive sensing," Proceeding of the 2014 IEEE International Conference on Acoustic, Speech, and Signal Processing, ICASSP 2014, 3370-3374, Italy, 2014.

25. Seng, C. H., A. Bouzerdoum, M. G. Amin, and S. L. Phung, "Probabilistic fuzzy image fusion approach for radar through wall sensing," IEEE Trans. Image Process., Vol. 22, No. 12, 4938-4951, Dec. 2013.
doi:10.1109/TIP.2013.2279953


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