2019-01-13
Shape Reconstruction of Unknown Targets Using Multifrequency Linear Sampling Method
By
Progress In Electromagnetics Research Letters, Vol. 81, 77-83, 2019
Abstract
This paper aims to estimate the shape of microwave scattering objects using linear sampling method (LSM) with multifrequency data. LSM is a simple, reliable linear inverse algorithm and uses multiview multistatic single frequency scattered field data measured around target objects. Despite its simplicity and computational effectiveness, the output LSM results depend on the frequency of operation. To improve the LSM performance, the present work proposes a new formulation that incorporates frequency information in the LSM equation. As a result, LSM finds the target's shape by a simple solution to a linear inverse problem via multifrequency data. The output results are tested with various types of numerical examples of synthetic data as well as experimental data provided by the Institute of Fresnel.
Citation
Mallikarjun Erramshetty, "Shape Reconstruction of Unknown Targets Using Multifrequency Linear Sampling Method," Progress In Electromagnetics Research Letters, Vol. 81, 77-83, 2019.
doi:10.2528/PIERL18110102
References

1. Pastorino, M., Microwave Imaging, 82-91, Wiley, 2010.
doi:10.1002/9780470602492

2. Colton, D., H. Haddar, and M. Piana, "The linear sampling method in inverse electromagnetic scattering theory," Inv. Prob., Vol. 19, 105-137, 2003.
doi:10.1088/0266-5611/19/6/057        Google Scholar

3. Catapano, I., F. Soldovieri, and L. Crocco, "On the feasibility of the linear sampling method for 3D GPR surveys," Progress In Electromagnetics Research, Vol. 118, 185-203, 2011.
doi:10.2528/PIER11042704        Google Scholar

4. Shelton, N. and K. F. Warnick, "Behavior of the regularized sampling inverse scattering method at internal resonance frequencies," Progress In Electromagnetics Research, Vol. 38, 29-45, 2002.
doi:10.2528/PIER02092502        Google Scholar

5. Mallikarjun, E. and A. Bhattacharya, "Shape reconstruction of mixed boundary objects by linear sampling method," IEEE Trans. Antennas Propagat., Vol. 63, No. 7, 3077-3086, 2015.
doi:10.1109/TAP.2015.2426679        Google Scholar

6. Sun, J., "An eigenvalue method using multiple frequency data for inverse scattering problems," Inv. Prob., Vol. 28, No. 8, 025012, 2012.
doi:10.1088/0266-5611/28/2/025012        Google Scholar

7. Guzina, B., F. Cakoni, and C. Bellis, "On the multi-frequency obstacle reconstruction via the linear sampling method," Inv. Prob., Vol. 26, No. 12, 125005, 2010.
doi:10.1088/0266-5611/26/12/125005        Google Scholar

8. Catapano, I., L. Crocco, and T. Isernia, "Improved sampling methods for shape reconstruction of 3-D buried targets," IEEE Trans. Geosci. Remote Sens., Vol. 46, No. 10, 3265-3273, 2008.
doi:10.1109/TGRS.2008.921745        Google Scholar

9. Colton, D. and H. Haddar, "An application of the reciprocity gap functional to inverse scattering theory," Inv. Probl., Vol. 21, No. 1, 383-398, 2005.
doi:10.1088/0266-5611/21/1/023        Google Scholar

10. Bozza, G., M. Brignone, and M. Pastorino, "Application of the no-sampling linear sampling method to breast cancer detection," IEEE Trans. Antennas Propag., Vol. 57, No. 10, 2525-2534, Oct. 2010.        Google Scholar

11. Catapano, I. and L. Crocco, "An imaging method for concealed targets," IEEE Trans. Geosci. Remote Sens., Vol. 47, No. 5, 1301-1309, May 2009.
doi:10.1109/TGRS.2008.2010773        Google Scholar

12. Belkebir, K. and M. Saillard, "Special section: Testing inversion algorithms against experimental data," Inv. Prob., Vol. 17, 1565-1571, 2001.
doi:10.1088/0266-5611/17/6/301        Google Scholar

13. Geffrin, J. M., P. Sabouroux, and C. Eyraud, "Free space experimental scattering database continuation: Experimental set-up and measurement precision," Inv. Probl., Vol. 21, S117-S130, 2005.
doi:10.1088/0266-5611/21/6/S09        Google Scholar