An optimal data fusion for distributed multisensor systems

Abstract

A general data fusion framework for distributed sensor systems of arbitrary redundancies is presented. By a general framework, we mean that the proposed method, referred to here as Covariance Extension (CE) Method, is able to directly fuse different dimensions of state vectors as well as constraints without additional processing. For instance, well-known Bar-Shalom Campo and Millman's formulae for the fusion of 2 and N correlated sensors, respectively, cannot properly handle state vectors of unequal dimensions. The proposed CE Method aggregates all the state vectors into a single vector in an extended space and transforms data fusion as a problem of satisfying the constraints among state vectors in the extended space. Specifically, the method orthogonally projects the mean and covariance of the aggregated state vectors on the equality constraint manifold in the extended space. The proposed CE Method is proven to be optimal in the sense of Minimum Mean Square Error (MMSE), while providing computational efficiency over Millman's formula. Simulation results shows the robust nature and effectiveness of the proposed method.