Event-Triggered Kalman Filter and Its Performance Analysis

In estimation of linear systems, an efficient event-triggered Kalman filter algorithm is proposed. Based on the hypothesis test of Gaussian distribution, the significance of the event-triggered threshold is given. Based on the threshold, the actual trigger frequency of the estimated system can be ac...

Full description

Saved in:
Bibliographic Details
Published inSensors (Basel, Switzerland) Vol. 23; no. 4; p. 2202
Main Authors Li, Xiaona, Hao, Gang
Format Journal Article
LanguageEnglish
Published Switzerland MDPI AG 15.02.2023
MDPI
Subjects
Accuracy
accuracy comparison
Algorithms
Analysis
Communication
Data transmission
event trigger
Kalman filters
Normal distribution
Sensors
state estimation
threshold setting
state estimation
event trigger
accuracy comparison
threshold setting
Online AccessGet full text

Cover

More Information
Summary:In estimation of linear systems, an efficient event-triggered Kalman filter algorithm is proposed. Based on the hypothesis test of Gaussian distribution, the significance of the event-triggered threshold is given. Based on the threshold, the actual trigger frequency of the estimated system can be accurately set. Combining the threshold and the proposed event-triggered mechanism, an event-triggered Kalman filter is proposed and the approximate estimation accuracy can also be calculated. Whether it is a steady system or a time-varying system, the proposed algorithm can reasonably set the threshold according to the required accuracy in advance. The proposed event-triggered estimator not only effectively reduces the communication cost, but also has high accuracy. Finally, simulation examples verify the correctness and effectiveness of the proposed algorithm.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:1424-8220
1424-8220
DOI:10.3390/s23042202