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Nonlinear State Estimation With Multisensor Stochastic Scheduling
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DOI：10.1109/TSMC.2021.3065132.png)
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Abstract
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In this article, the problem of a nonlinear system states estimation with multisensor stochastic scheduling is investigated. In order to solve the non-Gaussian property induced by the nonlinear transformation, the unscented transformation (UT) technique is applied. Since the sensor networks channel is limited, the stochastic event-triggered mechanisms (SETMs) are proposed to reduce the network transmission burden. Under the SETMs, the modified unscented Kalman filter is proposed. Additionally, the sufficient conditions are given to guarantee the stabilities of the error covariance and the estimation error. Finally, extensive examples are carried out. Performances evaluation and comparison with existing methods are given to demonstrate the superiority of the proposed methods.
Keywords:
State estimation
Estimation error
Transmission line measurements
Nonlinear systems
Kalman filters
Technological innovation
Measurement uncertainty
Modified unscented Kalman filter (UKF)
multisensor
stochastic event-triggered mechanisms (SETMs)
unscented transformation (UT) technique
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