A new L-step neighbourhood distributed moving horizon estimator

Antonello Venturino (1) , Sylvain Bertrand (1) , Cristina Stoica Maniu (2) , Teodoro Alamo (3) , Eduardo Camacho (3)

1 DTIS, ONERA, Université Paris Saclay [Palaiseau]
2 L2S - Laboratoire des signaux et systèmes
3 Universidad de Sevilla = University of Seville

This paper focuses on Distributed State Estimation over a peer-to-peer sensor network composed by possible lowcomputational sensors. We propose a new-step Neighbourhood Distributed Moving Horizon Estimation technique with fused arrival cost and pre-estimation, improving the accuracy of the estimation, while reducing the computation time compared to other approaches from the literature. Simultaneously, convergence of the estimation error is improved by means of spreading the information amongst neighbourhoods, which comes natural in the sliding window data present in the Moving Horizon Estimation paradigm.

Domaines: Automatique / Robotique

Antonello Venturino, Sylvain Bertrand, Cristina Stoica Maniu, Teodoro Alamo, Eduardo Camacho. A new L-step neighbourhood distributed moving horizon estimator. IEEE 60th Conference on Decision and Control (CDC 2021), Dec 2021, Austin, United States. ⟨10.1109/cdc45484.2021.9682837⟩. ⟨hal-03358956⟩