Distributed moving horizon state estimation for sensor networks with low computation capabilities

Antonello Venturino (1, 2) , Cristina Stoica Maniu (1) , Sylvain Bertrand (3) , Teodoro Alamo (4) , Eduardo F Camacho (4)

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

This paper focuses on distributed state estimation for sensor network observing a discrete-time linear system. The provided solution is based on a Distributed Moving Horizon Estimation (DMHE) algorithm with consensus-based arrival cost and a pre-estimating Luenberger observer in the formulation of the local problem solved by each sensor. This leads to reduce the computation load, while preserving the accuracy of the estimation. Moreover, observability properties of local sensors are used for tuning the weights related to consensus information fusion built on an observability rank-based condition, in order to improve the convergence of the estimation error. Results obtained by Monte Carlo simulations are provided to compare the performance with existing approaches, in terms of accuracy of the estimations and computation time.

Mots clés: distributed state estimation; moving horizon estimation; Luenberger observer; sensor network; linear systems

Domaines: Automatique / Robotique

Antonello Venturino, Cristina Stoica Maniu, Sylvain Bertrand, Teodoro Alamo, Eduardo F Camacho. Distributed moving horizon state estimation for sensor networks with low computation capabilities. System Theory, Control and Computing Journal, 2021, 1 (1), pp.81-87. ⟨10.52846/stccj.2021.1.1.14⟩. ⟨hal-03325770⟩