Malicious attacks on state estimation in multi-sensor dynamic systems

Abstract

In this paper, the problem of false information injection attack on the Kalman filter in dynamic systems is investigated. It is assumed that the Kalman filter system has no knowledge of the existence of the attacks. To be concrete, a target tracking system is used as an example in the paper. 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. The optimal solutions are solved by maximizing the determinant of the mean squared estimation error matrix, under a constraint on the total power of the injected bias noises. Numerical results are also provided in order to illustrate the effectiveness of the proposed attack strategies.