DocumentCode :
2859819
Title :
On the stability and optimality of distributed Kalman filters with finite-time data fusion
Author :
Khan, U.A. ; Jadbabaie, A.
Author_Institution :
Dept. of Electr. & Comput. Eng., Tufts Univ., Medford, MA, USA
fYear :
2011
fDate :
June 29 2011-July 1 2011
Firstpage :
3405
Lastpage :
3410
Abstract :
In this paper, we consider distributed estimation for discrete-time, linear systems, with finite-time data fusion of agent measurements 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 average-consensus, or under restricted observability requirements (one-step and/or local), 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 on agent measurements, 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 in a stable distributed estimator that can be implemented using semi-definite programming at each agent. We further characterize the performance of such distributed estimators by 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 gain 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.
Keywords :
Kalman filters; discrete time systems; linear systems; observability; parameter estimation; sensor fusion; stability; agent measurements; centralized performance gap; discrete time linear system; distributed Kalman filters; distributed performance gap; finite time data fusion; infinite horizon Kalman gain; information matrices; local observation maps; restricted observability requirements; semidefinite programming; stable distributed estimator; Covariance matrix; Distributed databases; Heuristic algorithms; Kalman filters; Noise; Observability; Stability analysis;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
American Control Conference (ACC), 2011
Conference_Location :
San Francisco, CA
ISSN :
0743-1619
Print_ISBN :
978-1-4577-0080-4
Type :
conf
DOI :
10.1109/ACC.2011.5991569
Filename :
5991569
Link To Document :
بازگشت