Consensus-based linear distributed filtering

Authors:
Ion Matei;John S. Baras
Affiliations:
Institute for Research in Electronics and Applied Physics, University of Maryland, College Park 20742, United States and Engineering Laboratory, National Institute of Standards and Technology, Gai ...;Institute for Systems Research and Department of Electrical and Computer Engineering, University of Maryland, College Park 20742, United States
Venue:
Automatica (Journal of IFAC)
Year:
2012

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Abstract

We address the consensus-based distributed linear filtering problem, where a discrete time, linear stochastic process is observed by a network of sensors. We assume that the consensus weights are known and we first provide sufficient conditions under which the stochastic process is detectable, i.e. for a specific choice of consensus weights there exists a set of filtering gains such that the dynamics of the estimation errors (without noise) is asymptotically stable. Next, we develop a distributed, sub-optimal filtering scheme based on minimizing an upper bound on a quadratic filtering cost. In the stationary case, we provide sufficient conditions under which this scheme converges; conditions expressed in terms of the convergence properties of a set of coupled Riccati equations.