Resumen: This paper presents ECO-DKF, the first E vent-Triggered and C ertifiable O ptimal D istributed K alman F ilter. Our algorithm addresses two major issues inherent to Distributed Kalman Filters: (i) fully distributed and scalable optimal estimation and (ii) reduction of the communication bandwidth usage. The first requires to solve an NP-hard optimisation problem, forcing relaxations that lose optimality guarantees over the original problem. Using only information from one-hop neighbours, we propose a tight Semi-Definite Programming relaxation that allows to certify locally and online if the relaxed solution is the optimum of the original NP-hard problem. In that case, ECO-DKF is optimal in the square error sense under scalability and event-triggered one-hop communications restrictions. Additionally, ECO-DKF is a globally asymptotically stable estimator. To address the second issue, we propose an event-triggered scheme from the relaxed optimisation output. The consequence is a broadcasting-based algorithm that saves communication bandwidth, avoids individual communication links and multiple information exchanges within instants, and preserves the optimality and stability properties of the filter. Idioma: Inglés DOI: 10.1109/TAC.2023.3331667 Año: 2023 Publicado en: IEEE TRANSACTIONS ON AUTOMATIC CONTROL (2023), 1-8 ISSN: 0018-9286 Factor impacto JCR: 6.2 (2023) Categ. JCR: ENGINEERING, ELECTRICAL & ELECTRONIC rank: 44 / 353 = 0.125 (2023) - Q1 - T1 Categ. JCR: AUTOMATION & CONTROL SYSTEMS rank: 12 / 84 = 0.143 (2023) - Q1 - T1 Factor impacto CITESCORE: 11.3 - Computer Science Applications (Q1) - Electrical and Electronic Engineering (Q1) - Control and Systems Engineering (Q1)