Ground Penetrating Radar

The first peer-reviewed scientific journal dedicated to GPR

Open access, open science

ISSN 2533-3100

Ground Penetrating Radar 2018, Volume 1, Issue 2, GPR-1-2-5,   https://doi.org/10.26376/GPR2018011


Model-based clutter reduction method for Forward Looking Ground Penetrating Radar imaging

Yukinori Fuse, Borja Gonzalez-Valdes, Jose A. Martinez-Lorenzo, and Carey M. Rappaport


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Abstract:   Model based imaging methods for a dual-band fully polarimetric vehicle-based Forward-Looking Ground Penetrating Radar (FLGPR) are presented. The radar consists of two fully polarimetric arrays of wideband horns - one at L-band and one at X-band - that form synthetic apertures as the vehicle advances. Model-based clutter suppression image processing is used to clean the Synthetic Aperture Radar (SAR) image obtained from the VV polarized L-band radar by employing a mixed binary mask. This clutter mask is formed from the second (X-band) frequency and the VH cross-polarized L-band responses. Receiver Operating Characteristic (ROC) curves using measured field data are used to evaluate the enhancement of the target signal to clutter ratio. The proposed methods reduce the false alarm rate and improve the detection performance of the system.


Keywords:  Imaging system; Synthetic Aperture Radar (SAR); Forward-Looking Ground Penetrating Radar (FLGPR).


Introduction

Ground Penetrating Radar (GPR) is widely used to detect subsurface objects [1] including explosive devices such as mines and Improvised Explosive Devices (IEDs). Other detection sensors include infrared (IR) cameras [2], [3], acoustic detectors [4], and laser-induced breakdown spectroscopy [5]. Forward-Looking Ground Penetrating Radar (FLGPR) has the advantages of sensing below the ground surface while having a large stand-off distance between the sensor systems and buried threats, and covering a wide detection area [6], [7]. These advantages lead to improved safety and efficiency for operators during the detection process. However, a major problem with FLGPR is clutter resulting from scattering from the rough ground surface; including large rocks, large depressions, and objects on the surface like trees, bushes, and manmade items. It is important to suppress this clutter from on- or above-ground objects in order to detect threatening objects below the ground surface and reduce the false alarm rate. Several methods have been proposed to solve this problem. One approach is based on extracting the characteristics and features of the target signal and classifying the received signals to extract the target signals from FLGPR image [8]–[10]. Another reduces the false alarms by combining FLGPR image results with other types the sensors. Combined FLGPR and IR sensor information has been studied in [11], [12]. The IR features are extracted from a vehicle mounted IR camera and IR images provide the clutter locations, which are not available in FLGPR, to eliminate false alarms. Another sensor combination (FLGPR and visible-spectrum colour camera) has been studied in [13]. The information from the visible camera is used for reducing the false alarms. FLGPR image data is used directly while the camera is used to extract the features of the target signals or eliminate the clutter. In this work, a model-based clutter suppression method is presented. The method is validated with field measurement data experimentally generated by the United States (US) Army, Communications-Electronics Research, Development and Engineering Center (CERDEC), Night Vision and Electronic Sensors Directorate (NVESD). The FLGPR system is a dual-band radar system with L-band (0.75 ~ 3.2 GHz) and X-band (8 ~ 12 GHz) radars (Table I).

The L-band radar is fully polarimetric while X-band radar is VV (vertical transmitting, vertical receiving) - polarized. The scattering from above-ground objects tends to be strong for both X-band and L-band radar, while the X-band and the VH (vertical transmitting, horizontal receiving) cross-polarization responses of buried targets are weak. This is due to higher frequency waves being attenuated as they propagate through soil, and the depolarizing effects of the wave refraction at the ground interface for VH scattered waves. Using X-band and VH L-band signals to identify clutter signals with minimal response from subsurface objects provides a method to uniquely distinguish buried targets. The model-based response at the receiving array due to the primary clutter objects is subtracted from the original VV polarized L-band signal, and a new clutter-suppressed Synthetic Aperture Radar (SAR) image is generated.


References

[1] D. J. Daniels, “A review of GPR for landmine detection,” Sensing and Imaging: An international journal, vol. 7, no. 3, pp. 90–123, September 2006, doi: 10.1007/s11220-006-0024-5.

[2] N. Playle, D. M. Port, R. Rutherford, I. A. Burch, and R. Almond, “Infrared polarization sensor for forward-looking mine detection,” 2002 AeroSense conference of the International Society for Optics and Photonics, Orlando, FL, US – Proceedings of the SPIE, vol. 4742: Detection and Remediation Technologies for Mines and Minelike Targets VII, August 2002, pp. 11–18, doi: 10.1117/12.479086.

[3] K. P. Gurton and M. Felton, “Remote detection of buried land-mines and ieds using lwir polarimetric imaging,” Optics Express, vol. 20, no. 20, pp. 22344–22359, September 2012, doi: 10.1364/OE.20.022344.

[4] R. D. Costley, J. M. Sabatier, and N. Xiang, “Forward-looking acoustic mine detection system,” 2001 Aerospace/Defense Sensing, Simulation, and Controls conference of the International Society for Optics and Photonics, Orlando, FL, US – Proceedings of the SPIE, vol. 4394: Detection and Remediation Technologies for Mines and Minelike Targets VI, October 2001, pp. 617–626, doi: 10.1117/12.445514.

[5] J. Moros, F. J. Fortes, J. M. Vadillo, and J. J. Laserna, “Libs detection    of explosives in traces,” Chapter 13 in Laser-Induced Breakdown Spectroscopy, Springer Series in Optical Sciences, vol. 182, pp. 349–376, 2014, ISBN 9783642450846.

[6] G. Liu, Y. Wang, J. Li, and M. R. Bradley, “SAR imaging for a forward- looking  GPR  system,”  2003 Aerospace/Defense Sensing, Simulation, and Controls conference of the International Society for Optics and Photonics, Orlando, FL, US –  Proceedings of the SPIE, vol. 5089: Detection and Remediation Technologies for Mines and Minelike Targets VIII, September 2003, pp. 322–333, doi: 10.1117/12.485687.

[7] T. Ton, D. Wong, and M. Soumekh, “Alaric forward-looking ground penetrating radar system with standoff capability,” Proceedings of the 2010 IEEE International Conference on Wireless Information Technology and Systems, 28 August – 3 September 2010, Honolulu, HI, US, pp. 1–4, doi: 10.1109/ ICWITS.2010.5611911.

[8] T. Wang, J. M. Keller, P. D. Gader, and O. Sjahputera, “Frequency subband processing and feature analysis of forward-looking ground- penetrating radar signals for land-mine detection,” IEEE Transactions on Geoscience and Remote Sensing, vol. 45, no. 3, pp. 718–729, February 2007, doi: 10.1109/TGRS.2006.888142.

[9] T. C. Havens, K. Ho, J. Farrell, J. M. Keller, M. Popescu, T. T. Ton, D. C. Wong, and M. Soumekh, “Locally adaptive detection algorithm for forward-looking ground-penetrating radar,” 2010 Defense, Security, and Sensing conference of the International Society for Optics and Photonics, Orlando, FL, US – Proceedings of the SPIE, vol. 7664:  Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XV, April 2010, Article ID 76642E, 9 pp., doi: 10.1117/12.851512.

[10] T. C. Havens, K. Stone, D. T. Anderson, J. M. Keller, K. Ho, T. T. Ton, D. C. Wong, and M. Soumekh, “Multiple kernel learning for explosive hazard detection in forward-looking ground-penetrating radar,” 2012 Defense, Security, and Sensing conference of the International Society for Optics and Photonics, Baltimore, MD, US – Proceedings of the SPIE, vol. 8357: Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XVII, May 2012, Article ID 83571D, 15 pp, doi: 10.1117/12.920482.

[11] D. Anderson, J. M. Keller, and O. Sjahputera, “Algorithm fusion in forward-looking long-wave infrared imagery for buried explosive hazard detection,” 2011 Defense, Security and Sensing symposium of the International Society for Optics and Photonics, Orlando, FL, US – Proceedings of the SPIE, vol. 8017: Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XVI, May 2011, Article ID 801722, doi: 10.1117/12.884600.

[12] K. Stone, J. Keller, K. Ho, M. Busch, and P. Gader, “On the registration of FLGPR and IR data for a forward-looking landmine detection system and its use in eliminating FLGPR false alarms,” 2008 Defense, Security and Sensing symposium of the International Society for Optics and Photonics, Orlando, FL, US – Proceedings of the SPIE, vol. 6953: Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XIII, April 2008, Article ID 695314, 12 pp. doi: 10.1117/12.782238.

[13] T. C. Havens, C. J. Spain, K. Ho, J. M. Keller, T. T. Ton, D. C. Wong,  and M. Soumekh, “Improved detection and false alarm rejection using FLGPR and color imagery in a forward-looking system,” in 2010 Defense, Security and Sensing symposium of the International Society for Optics and Photonics, Orlando, FL, US – Proceedings of the SPIE, vol. 7664: Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XV, April 2010, Article ID 76641U, 12 pp., doi: 10.1117/12.852274.

[14] B. Gonzalez-Valdes, Y. Alvarez, J. A. Martinez-Lorenzo, F. Las-Heras, and C. M. Rappaport, “On the combination of SAR and model based techniques for high-resolution real-time two-dimensional reconstruction,” IEEE Transactions on Antennas and Propagation, vol. 62, no. 10, pp. 5180–5189, October 2014, doi: 10.1109/TAP.2014.2346203.

[15] Y. Fuse, B. Gonzalez-Valdes, J. A. Martinez-Lorenzo, and C. M. Rappaport, “Advanced SAR imaging methods for forward-looking ground penetrating radar,” Proceedings of the 10th European Conference on Antennas and Propagation (EuCAP 2016), Davos, Switzerland, 10–15 April 2016, pp. 1–4, doi: 10.1109/EuCAP.2016.7481192.


Share & Cite this article

Unrestricted use, distribution, and reproduction in any medium of this article is permitted, provided the original article is properly cited.   Please cite this article as follows: Y. Fuse, B. Gonzalez-Valdes, J. A. Martinez-Lorenzo, and C. M. Rappaport, "Model-based clutter reduction method for forward looking Ground Penetrating Radar imaging,"  Ground Penetrating Radar, Volume 1, Issue 2, Article ID GPR-1-2-5, July 2018, pp. 96-112, doi.org/10.26376/GPR2018011.


Read further papers published by the same Authors on Ground Penetrating Radar

Yukinori Fuse:

Y. Fuse, M. Rostami, B. Gonzalez-Valdes, and C. M. Rappaport, "False alarm reduction by target tracking for Forward Looking Ground Penetrating Radar," Ground Penetrating Radar, Volume 1, Issue 2, Article ID GPR-1-2-6, July 2018, pp. 113-132, doi.org/10.26376/GPR2018012.

Borja Gonzalez-Valdes:

Y. Fuse, M. Rostami, B. Gonzalez-Valdes, and C. M. Rappaport, "False alarm reduction by target tracking for Forward Looking Ground Penetrating Radar," Ground Penetrating Radar, Volume 1, Issue 2, Article ID GPR-1-2-6, July 2018, pp. 113-132, doi.org/10.26376/GPR2018012.

Carey M. Rappaport:

Y. Fuse, M. Rostami, B. Gonzalez-Valdes, and C. M. Rappaport, "False alarm reduction by target tracking for Forward Looking Ground Penetrating Radar," Ground Penetrating Radar, Volume 1, Issue 2, Article ID GPR-1-2-6, July 2018, pp. 113-132, doi.org/10.26376/GPR2018012.

 


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