A Bayesian Approach to Data Fusion in Sensor Networks

Abstract: In this paper, we address the fusion problem in wireless sensor networks, where the cross-correlation between the estimates is unknown. To solve the problem within the Bayesian framework, we assume that the covariance matrix has a prior distribution. We also assume that we know the covariance of each estimate, i.e., the diagonal block of the entire covariance matrix (of the random vector consisting of the two estimates). We then derive the conditional distribution of the off-diagonal blocks, which is the cross-correlation of our interest. We show that when there are two nodes, the conditional distribution happens to be the inverted matrix variate $t$-distribution, from which we can readily sample. For more than two nodes, the conditional distribution is no longer the inverted matrix variate $t$-distribution. But we show that we can decompose it into several sampling problems, each of which is the inverted matrix variate $t$-distribution and therefore we can still sample from it. Since we can sample from this distribution, it enables us to use the Monte Carlo method to compute the minimum mean square error estimate for the fusion problem. We use two models to generate experiment data and demonstrate the generality of our method. Simulation results show that the proposed method works better than the popular covariance intersection method.