Sensors, Vol. 22, Pages 9044: OHetTLAL: An Online Transfer Learning Method for Fingerprint-Based Indoor Positioning

Sensors, Vol. 22, Pages 9044: OHetTLAL: An Online Transfer Learning Method for Fingerprint-Based Indoor Positioning Sensors doi: 10.3390/s22239044 Authors: Hailu Tesfay Gidey Xiansheng Guo Ke Zhong Lin Li Yukun Zhang In an indoor positioning system (IPS), transfer learning (TL) methods are commonly used to predict the location of mobile devices under the assumption that all training instances of the target domain are given in advance. However, this assumption has been criticized for its shortcomings in dealing with the problem of signal distribution variations, especially in a dynamic indoor environment. The reasons are: collecting a sufficient number of training instances is costly, the training instances may arrive online, the feature spaces of the target and source domains may be different, and negative knowledge may be transferred in the case of a redundant source domain. In this work, we proposed an online heterogeneous transfer learning (OHetTLAL) algorithm for IPS-based RSS fingerprinting to improve the positioning performance in the target domain by fusing both source and target domain knowledge. The source domain was refined based on the target domain to avoid negative knowledge transfer. The co-occurrence measure of the feature spaces (Cmip) was used to derive the homogeneous new feature spaces, and the features with higher weight values were selected for training the classifier because they could positively affect the location prediction of the targ...
Source: Sensors - Category: Biotechnology Authors: Tags: Article Source Type: research