Pareto Optimal Strategies for Event-triggered Estimation

This work considers the problem of resource-performance trade-off analysis for a system consisting of an active agent and a sensor network. Specifically, we focus on Event-triggered (ET) estimation, which allows communication of measurements only when deemed useful, e.g., when Kalman filter innovations exceed some threshold. We introduce a framework for ET-threshold strategy synthesis that optimally trades off resource consumption with task performance. Our approach is based on a novel belief space discretization technique that abstracts a continuous-space dynamics model for ET estimation to a finite Markov decision process. We show this abstraction method is more efficient than alternatives and also benefits from interpretability. Then, we leverage off-the-shelf tools to compute the set of all optimal trade-offs between resource consumption and task performance. Given a choice from this set, we synthesize an ET-threshold strategy that achieves the desirable behavior. Simulated results show our approach identifies nontrivial trade-offs between performance and energy savings, with only modest computational effort.