LAUSR.org creates dashboard-style pages of related content for over 1.5 million academic articles. Sign Up to like articles & get recommendations!

Distributed non-fragile set-membership filtering for nonlinear systems under fading channels and bias injection attacks

Photo by hakannural from unsplash

In this paper, the secure distributed set-membership filtering problem is investigated for general nonlinear system over wireless sensor networks. For the purpose of getting close to practical wireless sensor networks,… Click to show full abstract

In this paper, the secure distributed set-membership filtering problem is investigated for general nonlinear system over wireless sensor networks. For the purpose of getting close to practical wireless sensor networks, both the bias injection attacks and the channel fading of wireless communication are taken into account in the procedure of filter design. By employing linear matrix inequality (LMI) technique and Taylor's expansion formula, the nonlinearity, the channel fading, the bias injection attacks and the non-fragile are handled simultaneously in the set-membership filtering framework and the filter design problem is addressed. For the pre-specified filtering performance, sufficient conditions are obtain to ensure the existence of desired filter, where the filter gains are acquired via solving certain recursive LMI. Furthermore, in order to look for the locally best performance, an optimal algorithm is proposed. Finally, a simulation example is given to demonstrate the effectiveness of our proposed secure filtering algorithm.

Keywords: set membership; membership filtering; bias injection; injection attacks

Journal Title: International Journal of Systems Science
Year Published: 2021

Link to full text (if available)


Share on Social Media:                               Sign Up to like & get
recommendations!

Related content

More Information              News              Social Media              Video              Recommended



                Click one of the above tabs to view related content.