Quaternion Kalman Filter for False Data Injection Attacks

被引：4

作者：

Lin, Dongyuan ^{[1
]}

Zhang, Qiangqiang ^{[1
]}

Chen, Xiaofeng ^{[2
]}

Qian, Junhui ^{[3
,4
]}

Yan, Wenxing ^{[1
]}

Wang, Shiyuan ^{[1
]}

机构：

[1] Southwest Univ, Coll Elect & Informat Engn, Chongqing 400715, Peoples R China

[2] Chongqing Jiaotong Univ, Dept Math, Chongqing 400074, Peoples R China

[3] Chongqing Univ, Sch Microelect & Commun Engn, Chongqing 400044, Peoples R China

[4] Chongqing Univ, Chongqing Key Lab Biopercept & Intelligent Informa, Chongqing 400044, Peoples R China

来源：

IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS | 2024年 / 71卷 / 03期

基金：

中国国家自然科学基金;

关键词：

Quaternion Kalman filter; generalized quaternion measurement model; false data; cyber attack; MODEL;

D O I：

10.1109/TCSII.2023.3318635

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Quaternion Kalman filter (QKF) can deal with the estimation issues existing in three/four dimensional space, effectively. However, when the measurements are attacked by multiplicative or/and additive false data injection (FDI), the performance of traditional QKF will be degraded dramatically. To this end, this brief first remodels the quaternion measurement equation with consideration of FDI attacks. Then, a novel QKFs to combat multiplicative or/and additive FDI attacks are developed using the quaternion linear estimation and generalized Hamilton real (GHR) calculus theories. Finally, simulations illustrate the high filtering accuracy of the proposed quaternion algorithms under FDI attacks.

引用

页码：1501 / 1505

页数：5

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