A GMM-Based Secure State Estimation Approach against Dynamic Malicious Adversaries

Cui Zhu,Yuxuan Li,Zeyuan Zang,Zile Wang,Huanming Zheng,Xue-Bo Jin

doi:10.1155/2022/2127102

Abstract

We consider the secure state estimation of linear time-invariant Gaussian systems subject to dynamic malicious attacks. An error compensator is proposed to reduce the impact of local error data on state estimation. Based on that, a new estimation algorithm based on the Gaussian mixture model (GMM) aiming at dynamic attacks is proposed, which can cluster the local state estimates autonomously and improve the remote estimation accuracy effectively. The superiority of the proposed algorithm is verified by numerical simulations.

Highlights

Cyberphysical systems (CPSs), such as transportation networks and smart grids, integrate sensing, computing, and control technologies with a communication infrastructure
In [10], a Bayesian network based on the wireless power transfer (WPT) system state estimation algorithm is proposed, which can estimate the WPT system states in a distributed way using the Bayesian tree structure
The contributions of this article are listed as follows: (1) A new error compensator is proposed to alleviate the influence of wrong data on state estimation, which can judge whether the beliefs generated by the expectation-maximum (EM) algorithm are accurate based on the observability of the system, and correct the doubtful beliefs

Summary

Introduction

Cyberphysical systems (CPSs), such as transportation networks and smart grids, integrate sensing, computing, and control technologies with a communication infrastructure. In [22], a dynamic combination strategy and a distributed Kalman filter are proposed, which improve the robustness of the system against random error data injection and replay attacks. A new GMM-based state estimation algorithm is presented, which can effectively improve the state estimation accuracy against the dynamic adversaries. (1) A new error compensator is proposed to alleviate the influence of wrong data on state estimation, which can judge whether the beliefs generated by the expectation-maximum (EM) algorithm are accurate based on the observability of the system, and correct the doubtful beliefs (2) By introducing the error compensator, a new GMMbased estimation algorithm is presented, which can improve the estimation accuracy effectively.

Problem Formulation

The GMM-Based State Estimation

Numerical Simulation

Conclusion