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oai:drum.lib.umd.edu:1903/195572017-06-30T04:05:32Zcom_1903_2224com_1903_12com_1903_2col_1903_2756col_1903_3

Chopra, Nikhil Prasad, Akshay Digital Repository at the University of Maryland University of Maryland (College Park, Md.) Systems Engineering 2017-06-22T06:42:00Z 2017-06-22T06:42:00Z 2017 https://doi.org/10.13016/M2KG48 http://hdl.handle.net/1903/19557 This thesis proposes a solution to the problem of resilient state estimation and sensor fusion in an autonomous micro air vehicle. The setup comprises of redundant sensors that measure the same physical signal. An adversary may spoof a subset of these sensors and send falsified readings to the controller, potentially compromising performance and safety of the system. This work integrates Brooks-Iyengar Sensor fusion algorithm with a generic state estimator as a method to thwart sensor attacks. The algorithm outputs a point estimate and a fusion interval based on an assumed set of faulty sensors. Finally, the thesis illustrates the usefulness of the resilient state estimator with a case study on a MAV flight dataset. en RESILIENT STATE ESTIMATION FOR MICRO AIR VEHICLES UNDER SENSOR ATTACKS Thesis Robotics Artificial intelligence Brooks Iyengar Extended Kalman Filter MAV Quadrotor Resilient State Estimator Sensor Fusion open.access