Heterogeneous Data Driven Manifold Regularization Model for Fingerprint Calibration Reduction

Accurate indoor localization is very important for various kinds of Location Based Services (LBS). For most of traditional approaches, the location estimation problems assume the availability of a vast amount of labeled calibrated data, which requires a great deal of manual effort. Previous researches cannot deal with this problem in both calibration reduction, location accuracy. In this paper, we propose a heterogeneous data driven manifold regularization model known as HeterMan to calibration-effort reduction for tracking a mobile node in a wireless sensor network. With the constraint of user heading orientation, we build a mapping function between signal space, physical space with extremely less labeled data, a large amount of unlabeled data. Experimental results show that we can achieve high accuracy with extremely less calibration effort comparing with previous methods. Furthermore, our method can reduce computation complexity, time consumption by parallel processing while maintaining high accuracy.