Pareto Optimal Strategies for Event-Triggered Estimation

TL;DR

This paper introduces a formal method to determine optimal event-triggered communication strategies in resource-constrained autonomous systems, balancing estimation accuracy and resource use effectively.

Contribution

It develops a belief space discretization technique to model event-triggered estimation as a Markov decision process with formal performance guarantees.

Findings

Identifies non-trivial trade-offs between resource consumption and estimation accuracy.

Provides a scalable approach for threshold selection in event-triggered estimation.

Demonstrates effectiveness through simulated results with modest computational effort.

Abstract

Although resource-limited networked autonomous systems must be able to efficiently and effectively accomplish tasks, better conservation of resources often results in worse task performance. We specifically address the problem of finding strategies for managing measurement communication costs between agents. A well understood technique for trading off communication costs with estimation accuracy is event triggering (ET), where measurements are only communicated when useful, e.g., when Kalman filter innovations exceed some threshold. In the absence of measurements, agents can use implicit information to achieve results almost as well as when explicit data is always communicated. However, there are no methods for setting this threshold with formal guarantees on task performance. We fill this gap by developing a novel belief space discretization technique to abstract a continuous space dynamics model for ET estimation to a discrete Markov decision process, which scalably accommodates threshold-sensitive ET estimator error covariances. We then apply an existing probabilistic trade-off analysis tool to find the set of all optimal trade-offs between resource consumption and task performance. From this set, an ET threshold selection strategy is extracted. Simulated results show our approach identifies non-trivial trade-offs between performance and energy savings, with only modest computational effort.