  Motivated by the need of observers that are both robust to disturbances and
guarantee fast convergence to zero of the estimation error, we propose an
observer for linear time-invariant systems with noisy output that consists of
the combination of N coupled observers over a connectivity graph. At each node
of the graph, the output of these interconnected observers is defined as the
average of the estimates obtained using local information. The convergence rate
and the robustness to measurement noise of the proposed observer's output are
characterized in terms of $\mathcal{KL}$ bounds. Several optimization problems
are formulated to design the proposed observer so as to satisfy a given rate of
convergence specification while minimizing the $H_\infty$ gain from noise to
estimates or the size of the connectivity graph. It is shown that that the
interconnected observers relax the well-known tradeoff between rate of
convergence and noise amplification, which is a property attributed to the
proposed innovation term that, over the graph, couples the estimates between
the individual observers. Sufficient conditions involving information of the
plant only, assuring that the estimate obtained at each node of the graph
outperforms the one obtained with a single, standard Luenberger observer are
given. The results are illustrated in several examples throughout the paper.
