Spatial latency reduction in GPR processing using stochastic sampling
Ground penetrating radar (GPR) is a promising technique for buried threat detection which provides a complimentary phenomenology to electro-magnetic induction (EMI) based sensing. However, many successful GPR-based buried threat detection algorithms require data collected both before and after an object of interest is encountered to make a declaration (typically this data is used to perform background normalization, or to adequately characterize the object's shape). Samples taken past an object of interest, but before a decision is made, constitute an algorithm's "spatial latency". For vehicular mounted antennae arrays, where vehicle stopping distance is a function of vehicle dynamics, driver responsiveness, and algorithmic spatial latency, reducing an algorithm's spatial latency can increase overall system safety and help keep operators out of harm's way. In this work we propose a stochastic sampling algorithm that can help reduce spatial latency for a wide range of GPR-based buried threat detection algorithms. © 2010 IEEE.
International Geoscience and Remote Sensing Symposium (Igarss)
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International Standard Book Number 13 (ISBN-13)
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