Attacking altitude estimation in drone navigation

Current counter-drone solutions have fairly limited capabilities, because they mostly use physical approaches or depend on specific drone implementations. In this paper, we analyze popular altitude estimation algorithms used in common drone navigation systems, identify their vulnerabilities, and propose several effective attacks. First, we carefully review current popular altitude estimation methods, and point out their weaknesses. Then, we develop a few attacks to two of the most popular altitude estimation methods: KF-based estimation and sensor-model-based estimation. For the KF-based estimation, we propose a maximum False Data Injection (FDI) attack and conduct a comprehensive analysis. We are currently developing a generic FDI attack based on this analysis. For the sensor-model-based estimation, we propose two attacks to expand its confidence intervals and manipulate its altitude estimations. We are currently evaluating these attacks and countermeasures, which will be presented soon.

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