This paper is concerned with the distributed fusion filtering problem for networked stochastic uncertain systems with white multiplicative noises. The measured data of each sensor are transmitted to the local processor over different communication channels with different one-step delay and packet dropout rates. At each moment, the local processor may receive one or two data packets or nothing. Local filters at individual local processors are transmitted to the fusion center to produce a fusion filter. First, a new augmented model with a lower-dimension state vector is developed. Based on the new augmented model, the local optimal linear filter is designed in the linear minimum variance sense. Then, the cross-covariance matrices between any two local filtering errors are derived to compute the fusion weights. At last, the distributed fusion filter is obtained based on the well-known fusion algorithm weighted by matrices in the linear minimum variance sense. Moreover, the steady-state behavior of the proposed distributed fusion filter is analyzed.
Financed by the National Centre for Research and Development under grant No. SP/I/1/77065/10 by the strategic scientific research and experimental development program:
SYNAT - “Interdisciplinary System for Interactive Scientific and Scientific-Technical Information”.