Multi-rate stochastic H ∞ filtering for networked multi-sensor fusion

• Published in: Automatica (Oxford) Vol. 46; no. 2; pp. 437 - 444
• Main Authors: Liang, Yan, Chen, Tongwen, Pan, Quan
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier Ltd 01.02.2010; Elsevier
• Subjects: [formula omitted] filtering; Applied sciences; Computer science; control theory; systems; Computer systems and distributed systems. User interface; Control theory. Systems; Exact sciences and technology; Modelling and identification; Multi-rate systems; Multi-sensor fusion; Networked control systems; Process control. Computer integrated manufacturing; Software; Unknown input observers; Unknown input observers; Multi-rate systems; Multi-sensor fusion; Networked control systems; H ∞ filtering; Unknown input; Error estimation; Random excitation; Sensor fusion; H; Linear time; Multiple target; Modeling; Absolute error; Time varying system; Observer; Linear estimation; System identification; Sampling; State estimation; filtering; Estimation error; Target tracking; Existence theorem; Probabilistic approach; Measurement sensor; H infinite control; Distributed system; Velocity sensor; Decoupling; Filter; Discrete time; Distributed control; Data fusion; Sensor array
• ISSN: 0005-1098; 1873-2836
• DOI: 10.1016/j.automatica.2009.11.019

This paper presents a multi-rate filtering problem for a class of networked multi-sensor fusion systems with packet dropouts (PDs): the state evolves according to a linear discrete-time model with norm-bounded unknown inputs (UIs), and its underlying period is h ; the p sensors are distributively deployed with different sampling periods n 1 h , … , n p h ; multi-rate sensor measurements, corrupted by UIs, are subject to stochastic PDs in the transmission to a fusion center for state estimation; the estimation is updated at the period m h . Different from the single-rate estimator design with PDs which are treated as stochastic parameters, a UI observer is proposed where PDs are represented as zero-mean white input noises of the linear time-variant estimation error system. The results on the existence of a stable observer are proposed. Due to insufficient design freedom for absolute error decoupling, we turn to designing an observer-based stochastic H ∞ filter. A numerical example of distributive multi-sensor target tracking is given to illustrate the proposed filter.