Open AccessState Estimation of Wireless Sensor Networks Under False Data Injection
Author(s) -
Qingchen Jin,
Xin Chen,
Pengfei Zhang,
Jing Yuan,
Shang Li
Publication year - 2022
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2216/1/012016
Subject(s) - kalman filter , wireless sensor network , computer science , covariance , state (computer science) , stability (learning theory) , process (computing) , tracking (education) , nonlinear system , interval (graph theory) , covariance matrix , wireless , filter (signal processing) , algorithm , real time computing , artificial intelligence , mathematics , machine learning , statistics , telecommunications , computer network , computer vision , psychology , pedagogy , physics , quantum mechanics , combinatorics , operating system
This paper studies the problem of false external data injection information transmitted through wireless sensor networks. Firstly, this paper models the nonlinear system and improves the unscented Kalman filter to solve the problem of false data injection in the transmission process replaces the abnormal innovation data by setting the innovation confidence interval in advance and deduces the error covariance of this method. Then the stability of the designed algorithm is verified. Finally, the effectiveness of the filtering algorithm designed in this paper is illustrated by tracking the state of the system and the mean square error of the simulation system.