Technical Communique: The optimality for the distributed Kalman filtering fusion with feedback

Authors:
Yunmin Zhu;Zhisheng You;Juan Zhao;Keshu Zhang;X.Rong Li
Affiliations:
Department of Mathematics, Sichuan University Chengdu, Sichuan 610064, People's Republic of China;Department of Computer Science, Sichuan University Chengdu, Sichuan 610064, People's Republic of China;Department of Mathematics, Sichuan University Chengdu, Sichuan 610064, People's Republic of China;Department of Mathematics, Sichuan University Chengdu, Sichuan 610064, People's Republic of China;Department of Electrical Engineering, University of New Orleans, New Orleans, LA 70148, USA
Venue:
Automatica (Journal of IFAC)
Year:
2001

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Abstract

A rigorous performance analysis is dedicated to the distributed Kalman filtering fusion with feedback for distributed recursive state estimators of dynamic systems. It is shown that the Kalman filtering track fusion formula with feedback is, like the track fusion without feedback, exactly equivalent to the corresponding centralized Kalman filtering formula. Moreover, the so-called P matrices in the feedback Kalman filtering at both local trackers and fusion center are still the covariance matrices of tracking errors. Although the feedback here cannot improve the performance at the fusion center, the feedback does reduce the covariance of each local tracking error. The above results can be extended to a hybrid track fusion with feedback received by a part of the local trackers.