volume | PIER Journals

Abstract

Compressive Sensing (CS) is a recently developed technique, which can reconstruct the sparse signal with an overwhelming probability, even though the signal is sampled at highly sub-Nyquist rate. Based on the observation that the electromagnetic scattering structure (ESS) of a metal landmine is composed of two scattering centers, whose geometrical parameters are tightly related to its physical dimensions, a new physics-based landmine discrimination approach is proposed in this paper. Firstly, the approach uses the Multi-Measurement Iterative Pixel Discrimination method to reconstruct the landmine's ESS in noisy environments. Secondly, the geometrical parameters of the landmine's ESS are extracted from the sparse image. Thirdly, landmine discrimination is conducted according to the measured geometrical features and apriori knowledge. Finally, the field experimental results demonstrate the effectiveness of the proposed approach.

1. Donoho, , D. L., "Compressed sensing," IEEE Transactions on Information Theory, Vol. 52, No. 4, 1289-1306, 2006.
doi:10.1109/TIT.2006.871582 Google Scholar

2. You, , Y., H. Xu, C.-S. Li, and L. Zhang, "Data acquisition and processing of parallel frequency SAR based on compressive sensing," Progress In Electromagnetics Research, Vol. 133, 199-215, 2013. Google Scholar

3. Li, , J., S. Zhang, and J. Chang, "Applications of compressed sensing for multiple transmitters multiple azimuth beams SAR imaging," Progress In Electromagnetics Research, Vol. 127, 259-275, 2012.
doi:10.2528/PIER12021307 Google Scholar

4. Gurbuz, , A. C., H. M. James, and R. S Waymond, "A compressive sensing data acquisition and imaging method for stepped frequency GPRs ," IEEE Transactions on Signal Processing, Vol. 57, No. 7, 2640-2650, 2009.
doi:10.1109/TSP.2009.2016270 Google Scholar

5. Wei, , S.-J., X.-L. Zhang, J. Shi, and G. Xiang, "Sparse reconstruction for SAR imaging based on compressed sensing," Progress In Electromagnetics Research, Vol. 109, 63-81, 2010.
doi:10.2528/PIER10080805 Google Scholar

6. Potter, , L. C., E. Ertin, J. T. Parker, and M. Cetin, "Sparsity and compressed sensing in radar imaging," Proceedings of the IEEE, Vol. 98, No. 6, 1006-1020, Jun. 2010.
doi:10.1109/JPROC.2009.2037526 Google Scholar

7. Wei, , S.-J., X.-L. Zhang, and J. Shi, "Linear array SAR imaging via compressed sensing," Progress In Electromagnetics Research, Vol. 117, 299-319, 2011. Google Scholar

8. Chen, , J., J. Gao, Y. Zhu, W. Yang, and P. Wang, "A novel image formation algorithm for high-resolution wide-swath spaceborne SAR using compressed sensing on azimuth displacement phase center antenna ," Progress In Electromagnetics Research, Vol. 125, 527-543, 2012.
doi:10.2528/PIER11121101 Google Scholar

9. Jin, , T., Z.-M. Zhou, and , "Imaging model of forward-looking ground penetrating radar with split-aperture transmitting configuration," 2nd Asian and Pacific Conference on Synthetic Aperture Radar,, 21-24, 2009. Google Scholar

10. Gonzalez-Huici, , M. A., U. Uschkerat, and , "GPR modeling for landmine detection," URSI International Symposium on Electromagnetic Theory,, 152-155, Aug. 2010. Google Scholar

11. Jin, , T., J. Lou, and Z. Zhou, "Extraction of landmine features using a forward-looking ground penetrating radar with MIMO array ," IEEE Transactions on Geoscience and Remote Sensing, Vol. 50, No. 10, 4135-4144, Oct. 2012.
doi:10.1109/TGRS.2012.2188803 Google Scholar

12. Figueiredo, , M. A. T., R. D. Nowak, and S. J. Wright, "Gradient Projection for sparse reconstruction: Application to compressed sensing and other inverse problems," Journal of Selected Topics in Signal Processing: Special Issue on Convex Optimization Methods for Signal Processing , Vol. 1, No. 4, 586-598, 2007.
doi:10.1109/JSTSP.2007.910281 Google Scholar

13. Batu, , O., M. Cetin, and , "Parameter selection in sparsity-driven SAR imaging," IEEE Transactions on Aerospace and Electronic Systems , Vol. 47, No. 4, 3040-3050, Oct. 2011.
doi:10.1109/TAES.2011.6034687 Google Scholar

14. Baraniuk, , R., "A lecture on compressive sensing," IEEE Signal Processing Magazine, Vol. 24, No. 4, 118-121, 2007.
doi:10.1109/MSP.2007.4286571 Google Scholar

15. Donoho, , D. L., Y. Tsaig, and , "Extensions of compressed sensing ," Signal Processing, Vol. 86, No. 3, 533-548, 2006.
doi:10.1016/j.sigpro.2005.05.027 Google Scholar

16. Potter, L. C., E. Ertin, J. T. Parker, and M. Cetin, "Sparsity and compressed sensing in radar imaging," Proceedings of the IEEE, Vol. 98, No. 6, 1006-1020, Jun. 2010.
doi:10.1109/JPROC.2009.2037526 Google Scholar

17. Keogh, E., J. Lin, and W. Truppel, "Clustering of time series subsequences is meaningless: Implications for previous and future research," Proceedings of the IEEE International Conference on Data Mining, 115-122, 2003.
doi:10.1109/ICDM.2003.1250910 Google Scholar

18. Anitori, L. and P. Hoogeboom, "False alarm probability estimation for compressive sensing radar," IEEE Radar Conference,, 206-211, May 2011. Google Scholar

19. Wei, , H.-Y. and M. Soleimani, "Four dimensional reconstruction using magnetic induction tomography: Experimental study," Progress In Electromagnetics Research,, Vol. 129, 17-32, 2012. Google Scholar

20. Happ, , L., F. Le, M. Ressler, and K. Kappra, "Low-frequency ultrawideband synthetic aperture radar: Frequency subbanding for targets obscured by the ground ," Proceedings of SPIE, Vol. 2747, 194-201, 1996.
doi:10.1117/12.243078 Google Scholar

21. Kositsky, , J., "Results from a forward-looking GPR mine detection system ," Proceedings of SPIE, Vol. 4038(II), 1077-1087, 2000.
doi:10.1117/12.396193 Google Scholar

22. Zeng, T., M. Cherniakov, and T. Long, "Generalized approach to resolution analysis in BSAR," IEEE Transactions on Aerospace and Electronic Systems,, Vol. 41, No. 2, 461-474, Apr. 2005.
doi:10.1109/TAES.2005.1468741 Google Scholar

Dr. Peng-Yu Wang

Dr. Qian Song

Dr. Zhi-Min Zhou