PROJECT TITLE :

Networked fusion kalman filtering with multiple uncertainties

ABSTRACT:

This paper investigates the problem of fusion filtering for a class of networked multisensor fusion systems with multiple uncertainties, as well as sensor failures, stochastic parameter uncertainties, random observation delays, and packet dropouts. A novel model is proposed to explain the random observation delays and packet dropouts, and a sturdy optimal fusion filter for the addressed networked multisensor fusion systems is meant using the innovation analysis method. The dimension of the designed filter is the same as that of the original system, which helps to cut back computation value compared with the augmentation methodology. Moreover, strong reduced-dimension observation-fusion Kalman filters are proposed to additional cut back the computation burden. Note that the designed fusion filter gain matrices can be computed off-line, as they rely only on the higher bounds of random delays and on the incidence chances of delays and sensor failures. Some sufficient conditions are presented for stability and optimality of the designed fusion filters, and a steady-state fusion filter is also given for the networked multisensor fusion systems. Simulations show the effectiveness of the proposed fusion filters.


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