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Sequential Fusion Estimation for Networked Multisensor Nonlinear Systems
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DOI：10.1109/TIE.2019.2927191.png)
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Abstract
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This article presents a sequential fusion approach for state estimation of networked multisensor nonlinear systems, where sensors and estimators are allowed to work asynchronously. Both sequential measurement fusion (SMF) and sequential state fusion (SSF) estimators are designed, where the unscented filtering method is used in the design of the local SMF estimator, and the matrix weighting method is applied in the design of the SSF estimator. It is shown that the proposed SSF estimators provide a satisfactory estimation precision that is close to the centralized batch state fusion (BSF) estimator, while requiring smaller computation burden as compared with the BSF estimator. Both simulations and experiments of a moving target tracking system are presented to show the effectiveness of the proposed sequential fusion methods.
Keywords:
Estimation
Sensor fusion
Nonlinear systems
Sensor systems
Time measurement
Nickel
Asynchronous estimation
nonlinear systems
optimal estimation
sensor fusion
sensor networks
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