Language-Aided State Estimation

TL;DR

This paper introduces a novel framework called Language-Aided Particle Filter (LAPF) that leverages natural language data from humans to improve state estimation in physical systems, demonstrated through water level estimation in an irrigation canal.

Contribution

The paper proposes a new particle filter framework that integrates natural language observations into state estimation, bridging human language data with physical system monitoring.

Findings

LAPF effectively incorporates natural language data into state estimation.

The framework improves water level estimation accuracy in irrigation canals.

Natural language observations enhance sensing capabilities in physical systems.

Abstract

Natural language data, such as text and speech, have become readily available through social networking services and chat platforms. By leveraging human observations expressed in natural language, this paper addresses the problem of state estimation for physical systems, in which humans act as sensing agents. To this end, we propose a Language-Aided Particle Filter (LAPF), a particle filter framework that structures human observations via natural language processing and incorporates them into the update step of the state estimation. Finally, the LAPF is applied to the water level estimation problem in an irrigation canal and its effectiveness is demonstrated.