ProbIN : Probabilistic Inertial Navigation
Monday May 23, 11:00 am; Bldg 23, Rm 212
Dr. Joy Ying Zhang, Assistant Research Professor, CMU
Summary: Numerous applications require accurate personal navigation for environments where neither GPS signals nor infrastructure beacons, such as WiFi, are available. Inertial navigation using low-cost sensors suffers from the noisy readings which leads to drifting errors over time. In this paper, we introduce a novel inertial navigation approach ProbIN using Bayesian probabilistic framework. ProbIN models the inertial navigation problem as a noise channel problem where we want to recover the actual motion/displacement of the user from the noisy sensor readings. Building on the top of dead reckoning, ProbIN learns a statistical model to map the noisy sensor readings to user’s displacements instead of using the double integral of the acceleration. ProbIN also builds a statistical model to estimate the a priori probability of a user’s trajectory pattern. Combining the mapping model and the trajectory model in a Bayesian framework, ProbIN searches for a trajectory that has the highest probability given the sensor input. Our experiments show that ProbIN signiﬁcantly reduces the error of inertial navigation using low-cost MEMS sensors in mobile phones.
About the Speaker: Dr. Joy Ying Zhang is an assistant research professor in Mobility Research Center at Carnegie Mellon University Silicon Valley. He received his Ph.D. from Language Technologies Institute of Carnegie Mellon University. Most of his research centers around applying statistical learning on natural language processing problems, in particular, statistical machine translation systems. He has developed the Pandora translation system, a full-scale two-way phrase-based statistical machine translation engine for mobile devices. This technology has been commercialized in the Jibbigo Speech Translator for iPhone, the first and so-far only voice-to-voice translation system that does not require network connection. His current research interests include applying statistical learning methods on mobile applications for user behavior modeling and behavior-aware mobile computing including indoor positioning, geo-trace modeling and lifelogging.