False information injection attack on dynamic state estimation in multi-sensor systems

In this paper, the impact of false information injection is investigated for linear dynamic systems with multiple sensors. It is assumed that the 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. 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 multi-sensor target tracking system with either position sensors or position and velocity sensors has been used as an example to illustrate the theoretical results.