Ground Penetrating Radar 2018, Volume 1, Issue 2, GPR-1-2-6,   https://doi.org/10.26376/GPR2018012

False alarm reduction by target tracking for Forward Looking Ground Penetrating Radar

Yukinori Fuse, Masoud Rostami, Borja Gonzalez-Valdes, and Carey M. Rappaport

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Abstract:   An algorithm based on tracking stationary buried objects with advancing platform views is shown to reduce false alarms for Forward-Looking Ground Penetrating Radar (FLGPR). First, the Synthetic Aperture Radar (SAR) processed image is cleaned using a model-based clutter suppression method by applying masks to suppress the clutter signals. The mask is generated by L-band VV (vertical transmitting, vertical receiving), and VH (vertical transmitting, horizontal receiving) polarizations and X-band VV polarization SAR image results. Second, target tracking is applied to the clutter suppressed SAR image. These images are compared based on the system positions and the possible clutter signals are eliminated. The total detection performance is evaluated by a Receiver Operating Characteristic (ROC) curve with measurement data. The proposed method achieves significant reduction of the false alarm rate and improves the detection performance of the FLGPR system.

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

Introduction

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 [1], acoustic sensor [2], and metal detector [3] 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 [6], 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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References 

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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, 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.

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

Yukinori Fuse:

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.

Borja Gonzalez-Valdes:

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.

Carey M. Rappaport:

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.

Citations