  In this paper, we propose a fault detection and isolation based attack-aware
multi-sensor integration algorithm for the detection of cyberattacks in
autonomous vehicle navigation systems. The proposed algorithm uses an extended
Kalman filter to construct robust residuals in the presence of noise, and then
uses a parametric statistical tool to identify cyberattacks. The parametric
statistical tool is based on the residuals constructed by the measurement
history rather than one measurement at a time in the properties of
discrete-time signals and dynamic systems. This approach allows the proposed
multi-sensor integration algorithm to provide quick detection and low false
alarm rates for applications in dynamic systems. An example of INS/GNSS
integration of autonomous navigation systems is presented to validate the
proposed algorithm by using a software-in-the-loop simulation.
