Abstract: Aiming at  the problems that wireless fidelity (WiFi) and bluetooth low energy (BLE) fingerprint localization methods required a large number of labeled training samples and that the accuracy and stability of single-mode localization were difficult to  meet the requirements of large-scale localization scenarios, we proposed a semi-supervised manifold constraint localization method that fused WiFi and  BLE signals. The experimental results show that compared with a single feature, the normalized variance of each dimension of the proposed  method is stable below 0.08, and the accuracy of localization is improved by about 25 percentage points.  When the semi-supervised learning method is used to construct manifold constraints separately, the number of labeled samples required in the localization process can be reduced by about 90%. Therefore,  this method can greatly reduce the  number of required label samples, and effectively improve the stability and accuracy of localization.

Key words: multi-feature fusion, semi-supervised learning, manifold regularization, wireless fidelity, bluetooth low energy