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

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 hh; the pp sensors are distributively deployed with different sampling periods n1h,…,nphn1h,…,nph; 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 mhmh. 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∞H∞ filter. A numerical example of distributive multi-sensor target tracking is given to illustrate the proposed filter.