Abstract
The ground Penetrating Radar (GPR) is a promising remote sensing modality for Antipersonnel Mine (APM) detection. However, detection of the buried APMs are impaired by strong clutter, especially the reflection caused by rough ground surfaces. In this paper, we propose a novel clutter suppression method taking advantage of the low-rank and sparse structure in multidimensional data, based on which an efficient target detection can be accomplished. We firstly created a multidimensional image tensor using sub-band GPR images that are computed from the band-pass filtered GPR signals, such that differences of the target response between sub-bands can be captured. Then, exploiting the low-rank and sparse property of the image tensor, we use the recently proposed Tensor Robust Principal Analysis to remove clutter by decomposing the image tensor into three components: a low-rank component containing clutter, a sparse component capturing target response, and noise. Finally, target detection is accomplished by applying thresholds to the extracted target image. Numerical simulations and experiments with different GPR systems are conducted. The results show that the proposed method effectively improves signal-to-clutter ratio by more than 20 dB and yields satisfactory results with high probability of detection and low false alarm rates.
Highlights
Being capable of sensing discontinuity of electromagnetic properties in the ground, Ground Penetrating Radar (GPR) has been used as nondestructive remote sensing modality in many applications, including tree root detection [1], frozen layer monitoring [2], structural assessment [3], infrastructure inspection [4], and landmine detection [5,6,7,8,9]
Since many Antipersonnel Mine (APM) are of low-metal content and shallow burial, the responses of APMs are often obscured by ground reflections, rendering target visualization and detection very difficult
Two datasets were collected and tested: one was acquired with an impulse GPR developed by our research group; the other was collected by a Stepped Frequency Continuous Wave (SFCW) GPR and published by the Georgia Technology Institute [37,38]
Summary
Being capable of sensing discontinuity of electromagnetic properties in the ground, Ground Penetrating Radar (GPR) has been used as nondestructive remote sensing modality in many applications, including tree root detection [1], frozen layer monitoring [2], structural assessment [3], infrastructure inspection [4], and landmine detection [5,6,7,8,9]. GPR detection of Antipersonnel Mines (APMs) is always challenging because of clutter contamination, such as antenna coupling, ground reflection, and friendly objects (rocks, voids, etc.) [10,11]. As a major source of clutter, the ground reflection is of large magnitude, and usually varies with position due to surface roughness [11]. In the model-base methods, clutter models are defined, and objects whose responses significantly deviate from the clutter models are detected as potential targets. These methods vary depending on the clutter model chosen to capture the characteristics of the clutter. In [14], a Wide-Sense Stationary (WSS)
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