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A Bayesian approach to covariance estimation and data fusion

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2 Author(s)
Zhiyuan Weng ; Dept. of Electr. & Comput. Eng., Stony Brook Univ., Stony Brook, NY, USA ; Djuric, P.M.

In this paper, we address the fusion problem of two estimates, 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 co-variance 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. The conditional distribution happens to be the inverted matrix variate t-distribution. We can readily sample from this distribution and use a Monte Carlo method to compute the minimum mean square error estimate for the fusion problem. Simulations show that the proposed method works better than the popular covariance intersection method.

Published in:

Signal Processing Conference (EUSIPCO), 2012 Proceedings of the 20th European

Date of Conference:

27-31 Aug. 2012