Mutual Information-Based Planning for Informative Windowed Forecasting of Continuous-Time Linear Systems

TL;DR

This paper develops a mutual information-based method for planning sensing resources to improve forecast accuracy of continuous-time linear systems over a future time window, extending existing smoothing techniques.

Contribution

It introduces a novel mutual information expression for windowed forecasting in continuous-time linear systems, leveraging fixed-interval smoothing and conditional independence.

Findings

Effective in weather forecasting scenarios with moving verification paths.

Improves sensor network scheduling for tracking multiple targets.

Demonstrates computational feasibility and practical benefits.

Abstract

This paper presents expression of mutual information that defines the information gain in planning of sensing resources, when the goal is to reduce the forecast uncertainty of some quantities of interest and the system dynamics is described as a continuous-time linear system. The method extends the smoother approach in [5] to handle more general notion of verification entity - continuous sequence of variables over some finite time window in the future. The expression of mutual information for this windowed forecasting case is derived and quantified, taking advantage of underlying conditional independence structure and utilizing the fixed-interval smoothing formula with correlated noises. Two numerical examples on (a) simplified weather forecasting with moving verification paths, and (b) sensor network scheduling for tracking of multiple moving targets are considered for validation of the proposed approach.