U. A. Khan and A. Jadbabaie, "On the stability and optimality of distributed Kalman filters with finite-time data fusion," in 2011 American Control Conference, San Francisco, CA, Jun. 2011, submitted.


In this paper, we consider distributed estimation for discrete-time, linear systems, with \emph{finite-time data fusion} between each time-step of the dynamics. Prior work in this context is related to average consensus, where either the data fusion is implemented for an infinite time (in general) to reach consensus, or under restricted observability requirements (one-step observability and/or local observability), whereas, our results hold under the broadest observability conditions ($n$-step global observability, where $n$ is the dimension of the dynamics). We show that after the finite-time data fusion, the observation map at each agent is a linear combination of the local observation maps. We then show that this new observation map is observable (if the data is fused for a sufficient number of iterations that we lower bound) resulting into a stable distributed estimator that can be implemented using semi-definite programming at each agent. We further characterize the performance of such distributed estimators using a novel method of comparing the positive-definiteness of their corresponding information matrices. The centralized and distributed performance gap, although cannot be written in closed form, can be computed using the infinite horizon Kalman gains of each filter. Finally, we consider special cases under which the performance of these distributed estimators is equal to the performance of the centralized Kalman filter.

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