Spatial Correlation Based Sensor Selection Schemes for Probabilistic Area Coverage
This paper develops an analytical model for probabilistic area coverage in terms of the target detection probability. A decision fusion framework is utilized to infer the presence or absence of the target. Analytical results are derived for the target detection and false alarm probabilities in the presence of correlated sensor noise. The spatially correlated sensor observations are utilized to select a subset of sensors to meet the specified area coverage. Two new sensor selection schemes are proposed for maximizing information theoretic measures such as joint entropy. The sensor selection schemes are analyzed extensively based on simulations. The results show that the proposed sensor selection scheme outperforms two state-of-the-art sensor selection schemes: constrained random sensor selection and disjoint random sensor selection.