  Distributed sensor networks often include a multitude of sensors, each
measuring parts of a process state space or observing the operations of a
system. Communication of measurements between the sensor nodes and estimator(s)
cannot realistically be considered delay-free due to communication errors and
transmission latency in the channels. We propose a novel stability-based method
that mitigates the influence of sensor network delays in distributed state
estimation for linear time-varying systems. Our proposed algorithm efficiently
selects a subset of sensors from the entire sensor nodes in the network based
on the desired stability margins of the distributed Kalman filter estimates,
after which, the state estimates are computed only using the measurements of
the selected sensors. We provide comparisons between the estimation performance
of our proposed algorithm and a greedy algorithm that exhaustively selects an
optimal subset of nodes. We then apply our method to a simulative scenario for
estimating the states of a linear time-varying system using a sensor network
including 2000 sensor nodes. Simulation results demonstrate the performance
efficiency of our algorithm and show that it closely follows the achieved
performance by the optimal greedy search algorithm.
