The fundamental goals of land-mine and small unexploded ordnance (UXO) detection are to achieve a high probability of detection (P-d) and a low probability of false alarm (P-fa). Conventional methods usually fulfill the first goal at the cost of a high P-fa. In our previous work (Collins et al., IEEE Trans. Geosci. Remote Sensing 37 (2) (March 1998) 811-819; Gao and Collins, Proceedings of SPIE, Orlando, FL, April 1998; Gao, Master's Thesis, Duke University, December, 1997), we have shown that a Bayesian decision theoretic approach can be applied to improve the detectibility of land mines and small UXO targets using a single spatial sample of the electromagnetic induction (EMI) sensor data. In this paper, we present an alternative approach which significantly improves P-d at a fixed P-fa by utilizing features that capture the physical nature of EMI data within a statistical signal processing framework. The method we develop is a two-dimensional generalized likelihood ratio test (2-D GLRT) which utilizes spatial information from the sensor output. To illustrate the performance improvement, results obtained with the 2-D GLRT detector are compared to those for the standard threshold test for single-channel time-domain sensor data, as well as the energy detector, the integral detector, and the single location generalized likelihood ratio test (I-D GLRT) detector for multi-channel time-domain EMI sensor data. (C) 2000 Elsevier Science B.V. All rights reserved.
