Detection of Deeply Buried Non-Metal Objects by Ground Penetrating Radar Using Non-Negative Matrix Factorization

The ground penetrating radar (GPR) signal for a deeply buried non-metal object is weak and often does not have a hyperbolic signature, making it difficult to detect with high confidence. This paper takes a blind source separation approach by using non-negative matrix factorization (NMF) to improve the detection of deeply buried non-metal objects. The proposed approach interprets the GPR signal return as the sum of two independent components from two different sources, the background and the object. NMF enables the separation of the object signal component from the composite and thereby improves the detection performance. Preliminary results from a test site in the United States indicate that the probability of detecting these objects is improved by more than 20% compared to the pre-screener, at a false alarm rate of 0.003/m(2).