Event-Triggered Sequential Fusion Filter for Nonlinear Multi-Sensor Systems With Correlated Noise Based on Observation Noise Estimation

In this paper, a sequential fusion filter estimator based on event-triggered (ET) multi-sensor nonlinear systems and Cubature Kalman filter, that is ETCKF-SF, is studied, in which the observation noise of each sensor is synchronously correlated, and the observation noise of each sensor is correlated...

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Bibliographic Details
Published in:IEEE sensors journal 2022-05, Vol.22 (9), p.8818-8829
Main Authors: Cheng, Guo-Rui, Ma, Meng-Chen, Tan, Li-Guo, Song, Shen-Min
Format: Article
Language:English
Subjects:
Algorithms
Correlated noise
Correlation
Energy dissipation
Estimation
estimator of observation noise
event-triggered mechanism
Extended Kalman filter
Kalman filters
multi-sensor nonlinear system
Noise
Nonlinear systems
Sensor fusion
Sensor systems
Sensors
sequential fusion filter
Series expansion
Taylor series
Technological innovation
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Summary:In this paper, a sequential fusion filter estimator based on event-triggered (ET) multi-sensor nonlinear systems and Cubature Kalman filter, that is ETCKF-SF, is studied, in which the observation noise of each sensor is synchronously correlated, and the observation noise of each sensor is correlated with the process noise at the previous moment. Because the energy of each sensor in the network system is limited, in order to reduce unnecessary sensor energy loss, an ET mechanism is used to reduce the communication rate from the sensor to the fusion center. Sequential fusion filter estimator based on ET mechanism multi-sensor nonlinear systems and innovation analysis is studied, firstly. At the same time, in the design process of the sequential fusion filter, the estimator of the observation noise based on the ET mechanism will also be designed. Secondly, the numerical implementation of the proposed algorithm will be derived based on the third-degree spherical-radial rule Cubature Kalman filter (CKF). Finally, the proposed algorithm is compared with the extended Kalman filter (EKF) based on Taylor series expansion. The effectiveness of the proposed algorithm is verified by the comparison of simulation examples.
ISSN:1530-437X
1558-1748
DOI:10.1109/JSEN.2022.3161802