DOI
https://doi.org/10.25772/DAAT-6210
Defense Date
2015
Document Type
Thesis
Degree Name
Master of Science
Department
Engineering
First Advisor
Ruixin Niu
Abstract
In this thesis, the impact of false information injection is investigated for linear dynamic systems with multiple sensors. It is assumed that the Kalman filter system is unaware of the existence of false information and the adversary is trying to maximize the negative effect of the false information on the Kalman filter's estimation performance. First, a brief introduction to the Kalman filter is shown in the thesis. We mathematically characterize the false information attack under different conditions. For the adversary, many closed-form results for the optimal attack strategies that maximize the Kalman filter's estimation error are theoretically derived. It is shown that by choosing the optimal correlation coefficients among the bias noises and allocating power optimally among sensors, the adversary could significantly increase the Kalman filter's estimation errors. To be concrete, a target tracking system is used as an example in the thesis. From the adversary's point of view, the best attack strategies are obtained under different scenarios, including a single-sensor system with both position and velocity measurements, and a multi-sensor system with position and velocity measurements. Under a constraint on the total power of the injected bias noises, the optimal solutions are solved from two perspectives: trace and determinant of the mean squared error matrix. Numerical results are also provided in order to illustrate the negative effect which the proposed attack strategies could inflict on the Kalman filter.
Rights
© The Author
Is Part Of
VCU University Archives
Is Part Of
VCU Theses and Dissertations
Date of Submission
5-8-2015