A Non-divergent Estimation Algorithm in the Presence of Unknown Correlations
نویسنده
چکیده
This paper addresses the problem of estimation when the cross-correlation in the errors between diierent random variables are unknown. A new data fusion algorithm, the Covariance Intersection Algorithm(CI), is presented. It is proved that this algorithm yields consistent estimates irrespective of the actual correlations. This property is illustrated in an application of decentralised estimation where it is impossible to consistently use a Kalman lter.
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