Outlier selection methods for improved bearings-only geolocation.

Bearings-only location estimation is a problem that has widespread applications. While the concept of bearings-only location estimation is not new, there are still many problems inherent to the process. There is high demand for a process that can reduce bias, remove outliers, and more accurately estimate emitter location using bearing data, exclusive of range. This thesis applies outlier removal methods, the parameterization of which are characterized within, to create more reliable data sets from noisy data which likely contain a significant percentage of outliers. Secondly, it uses statistical estimation and resampling to create a small, plausible ellipse representing the location of the emitter being tracked. In this thesis simulation studies show these methods to be a significant improvement over the standard implementation of Cartesian Pseudo-Linear Estimation with the presence of outlier data.