## Distributed Estimation via Dual DecompositionS. Samar, S. Boyd, and D. Gorinevsky
The focus of this paper is to develop a framework for distributed estimation
via convex optimization. We deal with a network of complex sensor subsystems
with local estimation and signal processing. More specifically, the sensor
subsystems locally solve a maximum likelihood (or maximum a posteriori
probability) estimation problem by maximizing a (strictly) concave
log-likelihood function subject to convex constraints. These local
implementations are not revealed outside the subsystem. The subsystems interact
with one another via convex coupling constraints. We discuss a distributed
estimation scheme to fuse the local subsystem estimates into a globally optimal
estimate that satisfies the coupling constraints. The approach uses dual
decomposition techniques in combination with the subgradient method to develop
a simple distributed estimation algorithm. Many existing methods of data fusion
are suboptimal, |