An Improved Algorithm under Error Correlation in Distributed Data Fusion

Abstract

In distributed data fusion, the correlation between every local estimate makes an impact on the result of fusion. This paper introduces a scalar of correlation coefficient to present the correlation between local estimates, and estimate a covariance matrix in the limit of correlation. The improved algorithm put forward to use the form of Bar shalom-Campo algorithm and partly estimate the limit of correlation in order to guarantee the consistency of fusion results and effectively utilize the information of correlation. By the comparison of the simulation experiments, the fusion accuracy of the proposed algorithm is proved to be more effective than that of the Bar shalom-Campo algorithm. DOI: http://dx.doi.org/10.11591/telkomnika.v11i10.3420