The problem of location estimation of a source of random signals using a network of sensors is considered. A novel maximum-likelihood estimation (MLE) based approach using copula functions is proposed. The measurements received at the sensors are often spatially correlated and characterized by a multivariate distribution. Using the theory of copulas, the joint parametric density of sensor observations (joint likelihood) is approximated assuming only the knowledge of the marginal likelihood functions of the sensor observations. The problem of selecting the best copula function to model the joint likelihood is approached as one of model selection and a model fusion strategy is used to reduce the effect of selection bias. An example involving source localization of a Poisson source is presented to illustrate the proposed approach and demonstrate its performance.
['Ashok Sundaresan', 'Pramod K. Varshney']
Location Estimation of a Random Signal Source Based on Correlated Sensor Observations