Ground Penetrating Radar
Buried explosive threats such as mines have been a problem for decades. Especially Improvised Explosive Devices (IEDs) are a significant problem and they are explosive devices assembled with conventional military weapon such as mines and projectiles and the detonating mechanism. Some types of sensors such as Ground Penetrating Radar (GPR), infrared sensor , acoustic sensor , and metal detector  have been studied and developed for a long time. Forward-Looking GPR (FLGPR) [4, 5] is also one of the approaches to detect these treats and with the advantages of a safe stand-off distance between the sensor vehicle platform and the buried threat, and wide area coverage. FLGPR must distinguish between target of interest and clutter, due to scattering from the rough ground surface, rocks, objects above the surface like trees, bushes, and more. Model-based clutter suppression method for FLGPR has been proposed to solve this problem.
In this work, a false alarm reduction method based on a target tracking with a model-based clutter suppression method is presented. The method is validated with a measurement data set provided by the United States (US) Army, Communications-Electronics Research, Development and Engineering Center (CERDEC), Night Vision and Electronic Sensors Directorate (NVESD). The FLGPR is a dual wideband radar system, which uses the lower frequency L-band (0.75~3.2 GHz) radar to sense subsurface objects, and the higher frequency X-band (8~12 GHz) radar to sense primarily the on-ground and above-ground scatterers. Model-based clutter suppression processing is able to clean the L-band Synthetic Aperture Radar (SAR) image using a mixture binary mask formed by L-band and X-band masks , with the binary mask covering just the clutter signals while excluding the buried target signals. The mask is applied to the L-band radar image and a new simulated response is generated. Primary clutter objects signals are subtracted from the original L-band signals, generating a clutter-suppressed SAR image with minimal reduction in buried target image intensity.
To reduce the false alarm rate further, a target tracking image processing method is proposed to supplement the model-based clutter suppression method. The tracking process is applied using SAR images at different Global Positioning System (GPS)-determined positions of the radar platform to track the buried target responses. This process is repeated for selected observation frames. Since grazing-incident refracted waves tend to be fairly independent of incident angle, underground objects tend to scatter similarly for most stand-off distances; and thus yield a consistent image, independent of platform position. This image consistency from the buried targets is a feature that is exploited to distinguish them from clutter objects.
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 B. J. Copenhaver, J. D. Gorhum, C. M. Slack, M. L. Barlett, T. G. Muir, and M. F. Hamilton, “Acoustic response of a buried landmine with a low grazing-angle source array, focused on the ground,” Proceedings of Meetings on Acoustics, Acoustical Society of America, vol. 19, no. 1, 2 June 2013, Montreal, Canada, Article ID 045067, 6 pp., doi: 10.1121/1.4799604.
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 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,” Proceedings of the Defense and Security Symposium of the International Society for Optics and Photonics – 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.
 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.
 R. Jain, R. Kasturi, and B. G. Schunck. “Machine vision,” Publishing House: McGraw-Hill, Inc.; Book Series: “Computer Science;” New York, 1 May 2008; ISBN-13: 978-0070320185; ISBN-10: 0070320187; 549 pp.
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