Controlled Sensing: A Myopic Fisher Information Sensor Selection Algorithm

TL;DR

This paper introduces a low-complexity, Fisher information-based sensor selection algorithm for state tracking in controlled dynamical systems with Gaussian observations, demonstrating near-optimal performance in simulations.

Contribution

It generalizes Fisher information for multi-valued discrete parameters and control inputs, providing a computationally efficient alternative to optimal strategies.

Findings

The proposed algorithm achieves near-optimal tracking performance.

Closed-form formula for Fisher information in the system model.

Numerical simulations validate effectiveness in physical activity tracking.

Abstract

This paper considers the problem of state tracking with observation control for a particular class of dynamical systems. The system state evolution is described by a discrete-time, finite-state Markov chain, while the measurement process is characterized by a controlled multi-variate Gaussian observation model. The computational complexity of the optimal control strategy proposed in our prior work proves to be prohibitive. A suboptimal, lower complexity algorithm based on the Fisher information measure is proposed. Toward this end, the preceding measure is generalized to account for multi-valued discrete parameters and control inputs. A closed-form formula for our system model is also derived. Numerical simulations are provided for a physical activity tracking application showing the near-optimal performance of the proposed algorithm.