Author ORCID Identifier:

https://orcid.org/0000-0001-9050-4103

Date of Graduation

5-2026

Document Type

Thesis

Degree Name

Master of Science in Electrical Engineering (MSEE)

Degree Level

Graduate

Department

Electrical Engineering

Advisor/Mentor

McCann, Roy

Committee Member

Mantooth, Alan

Second Committee Member

Wu, Jingxian

Keywords

GPS; IMU; Kalman Filter; Navigation; Sensor Fusion; State Estimation

Abstract

Unmanned Aerial Vehicles (UAVs) like quadcopters are widely used in civilian and military applications such as farming, photography, search and rescue missions, and autonomous navigation and motion. The performance and stability of these systems depend on accurate state estimation, which provides real-time information about the vehicle’s position, velocity, and orientation. However, achieving reliable state estimation is challenging due to sensor noise, measurement uncertainty, and inaccurate modeling. Common navigation sensors such as the Global Positioning System (GPS) and Inertial Measurement Units (IMUs) exhibit complementary characteristics. GPS provides absolute position measurements but suffers from noise, multipath effects, and low update rates, while IMUs provide high-frequency motion measurements but accumulate drift over time due to integration errors. This thesis investigates the use of a linear Kalman filter to improve quadcopter state estimation through GPS and IMU sensor fusion. A six-degree-of-freedom quadcopter dynamic model is derived and linearized around a hovering equilibrium point, resulting in a linear state-space representation suitable for estimator design. The use of MATLAB/SIMULINK to compare the GPS-only, IMU-only, and GPS-aided IMU and result simulations shows that Kalman filter-based sensor fusion improves trajectory tracking accuracy by reducing accumulated drift and filtering noisy measurements.

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