Abstract-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 branchand-bound search, which further improves the pruning performance.
To minimize the estimation error over multiple time steps in a computationally tractable fashion, Huber REF proposed an information-based pruning algorithm for multi-step sensor scheduling by using the information matrices of the sensors and the monotonicity of the Riccati equation.
Optimal Pruning for Multi-Step Sensor Scheduling
IEEE Transactions on Automatic Control
IEEE Transactions on Automatic Control
Mathematics Computer Science