Electrooculography (EOG) can be used to measure eye movements while the eyelids are open or closed and to assist in the diagnosis of certain eye diseases. However, challenges in biosignal acquisition and processing lead to limited accuracy, limited resolution (both temporal and spatial), as well as difficulties in reducing noise and detecting artifacts. Methods such as finite impulse response, wavelet transforms, and averaging filters have been used to denoise and enhance EOG measurements. However, these filters are not specifically designed to detect saccades, and so key features (e.g., saccade amplitude) can be over-filtered and distorted as a consequence of the filtering process. Here we present a model-based fusion technique to enhance saccade features within noisy and raw EOG signals. Specifically, we focus on Westheimer (WH) and linear reciprocal (LR) eye models with a Kalman filter. EOG signals were measured using OpenBCI's Cyton Board (at 250 Hz), and these measurements were compared with a state-of-the-art EyeLink 1000 (EL; 250 Hz) eye tracker. On average, the LR model-based KF produced a 47% improvement of measurement accuracy over the bandpass filters. Thus, we conclude that our LR model-based KF outperforms standard bandpass filtering techniques in reducing noise, eliminating artifacts, and restoring missing features of saccade signatures present within EOG signals.