Theoretical Performance Bounds for Reduced-Order Linear and Nonlinear Distributed Estimation

In sensor networks deployed over large-scale, multidimensional physical systems with limited spatial observability, reduced-order, distributed estimation is a practical alternative to centralized estimation. For such reduced-order systems, centralized computation of the posterior Cramer Rao Lower Bound (CRLB) is not possible as the global estimate of the entire state vector is not accessible at a single processing node. The authors derive the distributed PCRLB (dPCRLB) implementations encompassing both linear and nonlinear reduced-order dynamical systems and verify their optimality through Monte Carlo simulations.

Provided by: Institute of Electrical & Electronic Engineers Topic: Mobility Date Added: Nov 2012 Format: PDF

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