Distributed estimation of non‐linear functions of the state vector for multisensory continuous‐time linear systems

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.

Distributed compressed sensing based joint detection and tracking for multistatic radar system

“…We adopt a centralized fusion and tracking framework for the multistatic radar system since the centralized algorithm involves a minimal information loss and achieves better accuracy and robustness [29,36,19], compared with the decentralized algorithm [24,2,48,37,47]. However, the existing centralized measurement fusion scheme has a high computational cost that prohibits it from practical applications.…”