Provably Efficient Sensor Allocation for Unknown High-dimensional Systems with Limited Sensing
It addresses the problem of efficient sensor allocation for observability in high-dimensional systems, which is critical for control and monitoring applications with limited sensing resources.
This paper proposes a two-stage framework for learning sensor allocations that ensure observability of unknown high-dimensional linear systems with a near-optimal number of sensors, overcoming limitations of existing methods that require many sensors or prior observable allocations.
This paper focuses on learning efficient sensor allocations that ensure observability of unknown high-dimensional linear systems using only a small number of sensors. Existing methods either require an impractically large number of sensors or assume access to an observable allocation in advance. We propose a two-stage framework that overcomes these limitations: first, a novel system identification algorithm integrates information from multiple trajectories, each observing different subsets of state coordinates; then, a classic sensor allocation method is adapted to operate on the learned system parameters. Our non-asymptotic guarantees show that the proposed approach learns a sensor allocation with a near-optimal number of sensors when sensors can be allocated on any state coordinate. We further extend the results to settings with inaccessible state coordinates that are unavailable for sensor allocation.