Work

Language-aided state estimation

Year: 2026

Type: article

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.

Source: IFAC Journal of Systems and Control

DOI: 10.1016/j.ifacsc.2026.100372

Authors: Yuki Miyoshi, Masaki Inoue, Yusuke Fujimoto

Institutions: Keio University, The University of Osaka

Language: English

Topic: Target Tracking and Data Fusion in Sensor Networks

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $2,890

Funder: Japan Society for the Promotion of Science