Privacy-Preserving Adaptive Consensus based Cubature Kalman Filter for Distributed Sensor Networks

Information security is an unsolved problem of existing distributed state estimation. In this paper, a privacy-preserving adaptive consensus-based cubature Kalman filter (PAC-CKF) with certain estimation accuracy and convergence speed is proposed to improve the information security of distributed se...

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Bibliographic Details
Published in2021 China Automation Congress (CAC) pp. 6874 - 6879
Main Authors Zha, Jirong, Han, Liang, Ren, Zhang
Format Conference Proceeding
LanguageEnglish
Published IEEE 22.10.2021
Subjects
adaptive average consensus
Adaptive systems
cubature Kalman filter
distributed estimation
Filtering algorithms
Information security
Kalman filters
Network security
Privacy
Privacy preservation
Real-time systems
sensor network
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Summary:Information security is an unsolved problem of existing distributed state estimation. In this paper, a privacy-preserving adaptive consensus-based cubature Kalman filter (PAC-CKF) with certain estimation accuracy and convergence speed is proposed to improve the information security of distributed sensor networks. By combining the state-decomposition mechanism with adaptive average consensus in the frame of cubature Kalman filter, the proposed algorithm can ensure both the network security and estimation accuracy under limited consensus iterations. Simulations are performed to demonstrate the effectiveness of estimation accuracy, privacy preservation, and convergence rate of the proposed algorithm.
ISSN:2688-0938
DOI:10.1109/CAC53003.2021.9727771