Open AccessOptimized distributed fusion filtering for singular systems with fading measurements and stochastic nonlinearity
Author(s) -
Chen Wang,
Jun Hu,
Hui Yu,
Dongyan Chen
Publication year - 2022
Publication title -
aims mathematics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.329
H-Index - 15
ISSN - 2473-6988
DOI - 10.3934/math.2022143
Subject(s) - invertible matrix , nonlinear system , singular value decomposition , fusion , algorithm , fading , computer science , transformation (genetics) , filter (signal processing) , matrix (chemical analysis) , basis (linear algebra) , mathematics , state (computer science) , control theory (sociology) , decoding methods , artificial intelligence , pure mathematics , quantum mechanics , geometry , control (management) , composite material , computer vision , gene , linguistics , philosophy , physics , biochemistry , chemistry , materials science
In this paper, the problem of optimized distributed fusion filtering is considered for a class of multi-sensor singular systems in the presence of fading measurements and stochastic nonlinearity. By utilizing the standard singular value decomposition, the multi-sensor stochastic singular systems are simplified to two reduced-order nonsingular subsystems (RONSs). The local filters (LFs) with corresponding error covariance matrices are proposed for RONSs via the innovation analysis approach. Then, on the basis of the matrix-weighted fusion estimation algorithm, the distributed fusion filters (DFFs) are designed for RONSs with multiple sensors in the linear minimum variance sense. Moreover, the DFFs are obtained by utilizing the state transformation for original singular systems. It can be observed that the DFFs have better accuracy in contrast with the LFs. Finally, an illustrate example is put forward to verify the feasibility of the proposed fusion filtering scheme.