GSTAMIDS ground-penetrating radar: hardware description

Sower, Gary D.; Eberly, John; Christy, Ed

doi:10.1117/12.445518

Cited by 2 publications

(2 citation statements)

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“…The impulse generator employed in the transmitter is designed to have a very fast rise and a slow decay in order to enhance both high-frequency and low-frequency components of the signals. The spectrum of the radar signal and further details of the hardware GPR system are documented in [31]. The receiver sampling system transforms the very fast realtime return signals from buried objects into much slower sampled-time data signals that are digitized, transmitted, and processed.…”

Section: Gpr Sensormentioning

confidence: 99%

“…This is accomplished by allowing each receiver antenna to operate with two samplers in each receiver operating simultaneously with its direct and left adjacent pulsers. We refer the reader to [31] for the details of this arrangement. In real-time operational mode, the vehicle that carries the GPR system advances at a speed of 9 km/h, and the radar provides a three-meter detection swath.…”

Section: Gpr Sensormentioning

confidence: 99%

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Real-Time Landmine Detection with Ground-Penetrating Radar Using Discriminative and Adaptive Hidden Markov Models

Frigui

Gader

2005

EURASIP J. Adv. Signal Process.

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We propose a real-time software system for landmine detection using ground-penetrating radar (GPR). The system includes an efficient and adaptive preprocessing component; a hidden Markov model- (HMM-) based detector; a corrective training component; and an incremental update of the background model. The preprocessing is based on frequency-domain processing and performs ground-level alignment and background removal. The HMM detector is an improvement of a previously proposed system (baseline). It includes additional pre- and postprocessing steps to improve the time efficiency and enable real-time application. The corrective training component is used to adjust the initial model parameters to minimize the number of misclassification sequences. This component could be used offline, or online through feedback to adapt an initial model to specific sites and environments. The background update component adjusts the parameters of the background model to adapt it to each lane during testing. The proposed software system is applied to data acquired from three outdoor test sites at different geographic locations, using a state-of-the-art array GPR prototype. The first collection was used as training, and the other two (contain data from more than 1200 m2 of simulated dirt and gravel roads) for testing. Our results indicate that, on average, the corrective training can improve the performance by about 10% for each site. For individual lanes, the performance gain can reach 50%

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Section: Gpr Sensormentioning

confidence: 99%

Section: Gpr Sensormentioning

confidence: 99%