(Georgia Institute of Technology, 2016-08-05)
Seywald, Kevin Lee
Measuring Earth’s gravitational field has important applications in fields ranging from
geodesy to exploration geophysics. Gravity field disturbances are typically no more than
100 mGal, hence requiring extremely precise sensors. The estimation of error sources
inherent in these sensors, such as bias, scale factor, and drift rate, significantly improve the
accuracy of these measurements, allowing for more precise gravity estimates. This research
builds upon prior work using a strapdown Inertial Navigation System (INS) paired with
Global Positioning Systems (GPS) for airborne platforms. In order to test and validate the
processing algorithms, various simulated test cases were created. Several refinements were
made to the traditional approach found in the literature, making the process more robust.
Most notably, an analytical solution was developed for the quaternion integration problem,
which is typically implemented using numerical methods. The analytical solution limits the
integration error to machine precision, and removes any error propagation. Furthermore,
the error equations implemented in the Kalman Filter were refined such that they better
capture the true dynamics of the error-states. These changes to the existing methodology
were validated by the proposed algorithm’s ability to accurately estimate the parameters
used to generate the simulated flight data.