Using machine learning to complement and extend the accuracy of UXO discrimination beyond the best reported results of the Jefferson proving ground technology demonstration

摘要

The accurate discrimination of unexploded ordnance from geophysical signals is very difficult. Research has demonstrated that using a machine learning technique known as linear genetic programming in concert with human expertise can extend the accuracy of unexploded ordnance discrimination past currently published results. This paper describes how linear genetic programming offers the promise of creating real-time unexploded ordnance discrimination.