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.