False data injection in Kalman Filters in an aerospace setting; ADS-B data with simulated noise

Report

Kalman Filters (KF) are recursive state estimation algorithms capable of combining and weighting deferent variables to estimate the real latent state of a system (Kalman, 1960). In this context, recursive reflects the property that not all previous data has to be kept in storage but every iteration incorporates information from previous observations and predictions (Maybeck, 1979). This made the KF widely applicable resulting in its implementation across various settings, including aerospace, submarines, and the estimation of missile trajectories (Grewal & Andrews, 2010). Given the importance and KFs across settings and systems there is growing interest from security researchers to understand the robustness of KFs under deferent adversarial models.

Authors: Roeling, M

• Publication status: Not published
• Peer review status: Not peer reviewed
• Journal: CDT Technical Papers
• Series: CDT Technical Papers
• Publication date: 2016-06-23
• Keywords: SBTMR