  In the considered linear Gaussian sensor scheduling problem, only one sensor
out of a set of sensors performs a measurement. To minimize the estimation
error over multiple time steps in a computationally tractable fashion, the
so-called information-based pruning algorithm is proposed. It utilizes the
information matrices of the sensors and the monotonicity of the Riccati
equation. This allows ordering sensors according to their information
contribution and excluding many of them from scheduling. Additionally, a tight
lower is calculated for branch-and-bound search, which further improves the
pruning performance.
