Standalone IMU positioning determining system for UAVs using artificial intelligenece

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Inertial Measurement Units (IMUs) are Micro-Electromechanical Systems (MEMS) that are able to provide acceleration angular orientation rates information via inertial sensing. Unlike other positioning devices like the Global positioning System (GPS), they do not require any form of communication with an external device or technology in order to obtain this information. This makes them the ideal positioning devices to serve as standalone systems. However, with certain drawbacks associated with the IMU they are unable to effectively serve in this role. Existing schemes employ the use of Kalman filters as a complementary approach to solve this issue but this also presents complexity and drawbacks resulting in the failure of the Kalman estimator especially when there is no GPS signal available. This paper proposes a technique by employing the use of an Artificial neural Network (ANN) to model certain state variables in order to estimate the position of an Unmanned Aerial Vehicle (UAV) quadrotor with the IMU serivng as a standalone positionng determing device.
Capstone Project submitted to the Department of Engineering, Ashesi University in partial fulfillment of the requirements for the award of Bachelor of Science degree in Electrical and Electronic Engineering, May 2021
drones, Inertial Measurement Unit