Indoor localization is an important field of research for advancing robotics and providing more accurate estimations of indoor locations for users. There are many indoor localization algorithm implementations, but many of them underperform under certain environmental changes or restrictions. This research will present a way of combining already existing indoor localization techniques to more accurately deduce the user’s location within a building. An experiment was conducted within campus building Pulse, where multiple fingerprints of locations where gathered, and then used to train and test the combined classification models. By fusing active acoustic location sensing and WiFi localization using weighted averaging, ensemble stacking, and 2-step localization, the combination of classifiers was able to outperform individual classifiers by up to 5% of localization accuracy. Additionally, 2-step localization and weighted averaging methods did not add any performance overhead.