Abstract
This study focuses on fusion algorithms for the estimation of a non-linear function of the state vector in a multisensory continuous-time stochastic system. The non-linear function of the state (NFS) represents a non-linear multivariate function of state variables, which can indicate useful information of a target system for control. To estimate a NFS using multisensory information, they propose one centralised and three distributed estimation fusion algorithms. For multivariate polynomial functions, they derive a closed-form estimation procedure. In the general case, an unscented transformation is used for evaluation of the fusion estimate of an NFS. The subsequent application of the proposed fusion estimators to a linear stochastic system within a multisensor environment demonstrates their effectiveness.
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