Multi-Sensor Scheduling for State Estimation with Event-Based, Stochastic Triggers

TL;DR

This paper introduces a stochastic event-triggered sensor scheduling method for multi-sensor systems that maintains Gaussian properties, enabling efficient and accurate state estimation despite communication constraints.

Contribution

It extends stochastic event-triggered scheduling to multi-sensor systems, preserving Gaussianity and improving state estimation under communication limits.

Findings

Preserves Gaussianity in multi-sensor scheduling

Enables exact MMSE estimation with stochastic triggers

Reduces communication while maintaining estimation accuracy

Abstract

In networked systems, state estimation is hampered by communication limits. Past approaches, which consider scheduling sensors through deterministic event-triggers, reduce communication and maintain estimation quality. However, these approaches destroy the Gaussian property of the state, making it computationally intractable to obtain an exact minimum mean squared error estimate. We propose a stochastic event-triggered sensor schedule for state estimation which preserves the Gaussianity of the system, extending previous results from the single-sensor to the multi-sensor case.