  This paper takes a different approach for the distributed linear parameter
estimation over a multi-agent network. The parameter vector is considered to be
stochastic with a Gaussian distribution. The sensor measurements at each agent
are linear and corrupted with additive white Gaussian noise. Under such
settings, this paper presents a novel distributed estimation algorithm that
fuses the the concepts of consensus and innovations by incorporating the
consensus terms (of neighboring estimates) into the innovation terms. Under the
assumption of distributed parameter observability, introduced in this paper, we
design the optimal gain matrices such that the distributed estimates are
consistent and achieves fast convergence.
