Outlier‐resistant state estimation for complex networks with random false data injection attacks under encoding–decoding mechanism

• Published in: International journal of adaptive control and signal processing Vol. 38; no. 8; pp. 2833 - 2854
• Main Authors: Liu, Yufeng, Hu, Jun, Jia, Chaoqing, Chen, Cai, Chi, Kun
• Format: Journal Article
• Language: English
• Published: Bognor Regis Wiley Subscription Services, Inc 01.08.2024
• Subjects: Algorithms; Coding; complex networks; Data analysis; Data transmission; Decoding; encoding–decoding mechanism; Error analysis; exponential boundedness in mean‐square sense; Impact analysis; Independent variables; Outliers (statistics); random false data injection attacks; Random variables; State estimation; Upper bounds
• ISSN: 0890-6327; 1099-1115
• DOI: 10.1002/acs.3833

Summary This article focuses on the outlier‐resistant state estimation problem for discrete time‐varying complex networks (TVCNs) affected by random false data injection attacks (FDIAs) under an encoding–decoding mechanism (EDM). From the perspective of information security, a uniform‐quantization‐based EDM is employed to encrypt the transmitted data. During the data transmission process, a set of independent random variables governed by Bernoulli distribution is introduced to characterize the occurrence of random FDIAs. For the purpose of alleviating the passive impact of potential measurement outliers, a saturation structure is adopted during the estimator design. The gain matrix is given by minimizing the upper bound of estimation error covariance. According to the stochastic analysis method, it is shown that the state estimation error is bounded exponentially in mean‐square sense by providing new sufficient condition. It should be noted that we make the first attempt to develop new outlier‐resistant state estimation method with performance evolution criterion in the time‐varying perspective for TVCNs with random FDIAs under EDM. Finally, a simulation example with comparative experiment is presented to illustrate the effectiveness of the newly presented outlier‐resistant estimation algorithm.