Estimating the location of a mobile device from wireless signal strength is an interesting research problem, especially given the complexity of signal propagation through space in the presence of obstacles such as buildings, walls, or people. Gaussian processes have already been used to solve such signal strength localization problems. We extend this work to indoor WiFi localization and present novel kernel functions which increase the accuracy of the Gaussian process model, especially when faced with sparse training data. We additionally present preliminary results of simultaneous mapping and localization using Gaussian process latent variable modeling.