Year: 2025
Type: conference-paper
Abstract: In this paper, we develop an event-triggered remote state estimator for nonlinear state-space models under networkinduced one-step randomly delayed measurements. Adopting event-triggering strategies reduces the transmission burden between the sensor and the estimator while maintaining the estimation accuracy. The developed method employs a particle filter to approximate the posterior distribution using particles and weights. In the non-triggering case, we use the constrained Bayesian estimation to compute the integrals associated with the posterior distribution. We evaluate the performance of the proposed algorithm using a simulated aircraft tracking problem. The results show that the proposed algorithm provides a comparable estimation accuracy to a particle filter without event triggering.
Institutions: Aalto University, University Ferhat Abbas of Setif
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4417051975