SiFu: A Generic and Robust Multimodal Signal Fusion Platform for Pervasive Localization

Abstract

We consider pervasive localization for a roaming user who may sample widely varying signal modes (GPS, WiFi, geomagnetism, Bluetooth low energy, etc.) and values over time and space. Previous works can only apply to specific (two or three) modes and environment, and cannot accommodate arbitrary signal modes and environmental changes due to signal noise, device heterogeneity, phone carriage states, etc. We propose SiFu, a simple, accurate, and generic multimodal signal fusion platform robust against environmental deviation from its design point. As a generic framework, SiFu treats a single-modal localization algorithm as a black box to embrace any existing, emerging or future signals with only incremental training. It employs Bayesian deep learning and data augmentation to mitigate the location bias of the single-modal localization algorithm and run-time deviation from the training data, respectively. Using a unified multimodal likelihood formulation and particle filter, it fuses with inertial sensor measurements for localization. We conduct extensive experiments in different venues (campus, mall, and subway station), and show that SiFu achieves significantly higher localization accuracy as compared to state-of-the-art (cutting the error by more than 20%). It is also robust against environmental variations (reducing error by 30%), even when the signal values deviate greatly from their original design settings.