Parametric signal estimation using sensor networks in the presence of node localization errors

Signal processing methods developed so far for sensor-network environments have ignored the effects of node localization inaccuracies. We propose a Bayesian framework that accounts for the inherent uncertainties in the node locations (caused by the node localization errors) and develop an estimation method that is robust to these uncertainties. We model the node localization errors as zero-mean Gaussian random vectors whose covariances are known up to a scaling factor. Iterated conditional modes (ICM) and Markov chain Monte Carlo (MCMC) algorithms are developed to estimate the signal parameters of interest and construct Bayesian confidence regions for these parameters. The proposed methods are then applied to localize an acoustic source using energy measurements. Numerical simulations demonstrate the performance of the proposed approach.