1Optimal Pruning for Multi-Step Sensor Scheduling
Marco F. Huber · 2016
Abstract—In the considered linear Gaussian sensor scheduling prob-lem, only one sensor out of a set of sensors performs a measurement. To minimize the estimation error over multiple time steps in a com-putationally 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. Index Terms—Kalman filtering, linear systems, sensor scheduling, sensor networks, stochastic optimal control I.