Optimal Pruning for Multi-Step Sensor Scheduling

TL;DR

This paper introduces an information-based pruning algorithm for multi-step sensor scheduling in linear Gaussian systems, improving computational efficiency by effectively ordering sensors and pruning the search space.

Contribution

The paper proposes a novel pruning algorithm that leverages information matrices and Riccati equation properties to optimize sensor scheduling over multiple time steps.

Findings

Enhanced pruning efficiency in sensor scheduling

Effective sensor ordering based on information contribution

Improved lower bounds for branch-and-bound search

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 branch-and-bound search, which further improves the pruning performance.