Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1999 · 48 citations · 0 references
Background Clutter DataEngineeringMine Detection AlgorithmsIntelligent SystemsData ScienceMine Detection HardwareInternet Of ThingsAutomatic Mine DetectionAutomatic Target RecognitionSynthetic Aperture RadarStructural Health MonitoringComputer EngineeringComputer ScienceSignal ProcessingRadarCivil EngineeringRemote SensingGround-penetrating RadarAutomated Mining
We describe several automatic mine detection algorithms in this paper. These methods were tested on real Ground Penetrating Radar (GPR) data and showed dramatic improvement in terms of probability of detection and false alarm rates compared to energy based techniques. The main contributions of this paper are as follows. (1) Only background clutter data, instead of mine data, are needed for the development of the algorithms, which makes collection of data for training and adapting the algorithms to new environment much easier than methods requiring both clutter and mine data for training. (2) The mine detection algorithms are developed in a fairly general form, and thus can be ported to other sensor platforms or future generations of mine detection hardware with little modification. (3) The algorithms require little on-line computation. (4) Adaptation of the algorithms to new environment or mine-fields is done automatically, which reduces human resources and the cost of training.