To address the problem of limited coverage area of wireless signals and difficulty in dealing with time-varying characteristics of wireless signals in traditional fingerprinting localization, we propose a method of using multi-source wireless signals for fingerprinting localization, and the accuracy of positioning, which is affected by the time varying of signals, is mitigated by geodesic flow kernel. Firstly, we construct our datasets by a multi-round random sampling of multiple wireless signal sources, which provides a richer and more diverse fingerprint features. Secondly, we fuse geodesic flow kernels from fingerprint features of different times, so that we extend transferring methods from two domains to multiple domains. Finally, base classifiers are trained on multiple datasets, and the predicted position are obtained from all base classifiers, so as to elevate the generalization of the model. Simulation results show that the proposed method outperforms the traditional approaches in terms of positioning accuracy.
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