Landmine detection and localization using chemical sensor array processing

We develop methods for the automatic detection and localization of landmines using chemical sensor arrays and statistical signal processing techniques. The transport of explosive vapors emanating from buried landmines is modeled as a diffusion process in a two layered system consisting of ground and air. The measurement and statistical models are derived by exploiting the associated concentration distribution. We derive a generalized likelihood ratio (GLR) detector and evaluate its performance in terms of the probabilities of detection and false alarm. To determine the unknown location of a landmine we derive a maximum likelihood (ML) estimation algorithm and evaluate its performance by computing the Cramer-Rao bound (CRB). The results are applied to the design of chemical sensor arrays, satisfying criteria specified in terms of detection and estimation performance measures, and to optimally select the number and positions of sensors and the number of time samples. To illustrate the potential of the proposed techniques in a realistic demining scenario, we derive a moving sensor algorithm in which the stationary sensor array is replaced by a single moving sensor. Numerical examples are given to demonstrate the applicability of our results.