.png){kind=icon}
Return
Dynamic event-based recursive filtering for networked systems under the encoding-decoding mechanism
.png){kind=icon}
PRE
AI
DOI：10.1016/j.jfranklin.2022.05.026.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
This paper focuses on the dynamic event-based recursive filtering problem for a class of time-varying networked systems under the encoding-decoding mechanism. For the purpose of saving energy consumption, a dynamic event-triggered protocol is applied to determine whether the measurement of the sensor is transmitted or not. In the transmission process of the measurement, a dynamic-quantization-based encoding-decoding mechanism is introduced to encrypt the transmitted measurement. In specific, the measurement outputs are first encoded into codewords which are then transmitted from the encoder to the decoder. After received by the decoder, the codewords are first decoded and then sent to the filter. A bounded uncertainty is introduced to characterize the difference between the original measurement and the decoded measurement. This paper is devoted to developing a recursive filtering algorithm for the considered system such that a minimal upper bound on the filtering error covariance is derived through appropriately designing filter gain. Moreover, the mean-square exponential boundedness of the filtering error is analyzed. Finally, the efficiency and superiority of the proposed algorithm are verified through two simulation examples. (C) 2022 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.
Keywords:
EXTENDED KALMAN FILTER
STATE ESTIMATION
CONSENSUS
Journal
J
IF:
3.7
Papers:
6.4K
Citations:
1.5W
Organization
Cited Papers
ShareSaveShareSave
ShareSaveShareSave
ShareSaveJoint state and fault estimation for time-varying nonlinear systems with randomly occurring faults and sensor saturations.png)
AUTOMATICA
AUTOMATICAIF5.9
PREAI
ShareSaveWind speed and wind energy forecast through Kalman filtering of Numerical Weather Prediction model outputAPPLIED ENERGY
APPLIED ENERGYIF11
PREAI
ShareSaveDynamic state estimation for islanded microgrids with multiple fading measurementsNEUROCOMPUTING
NEUROCOMPUTINGIF6.5
PREAI
ShareSave
View more