Abstract: The construction of radio map is time consuming and labor intensive in the conventional wireless local area network (WLAN) indoor localization systems. In order to solve this problem, the paper proposes a semi-supervised manifold alignment radio map construction approach based on the global geometry of manifold structure. The proposed method utilizes a small number of labeled RSS which requires a huge time consuming to collect and plenty of unlabeled data that is easy to obtain. Then, the locations of plenty of unlabeled data can be obtained by calibrating the solution of the manifold alignment of objective function. In addition, the geodesic distance is utilized to capture the global geometry of manifold feature which can fully exploit the correspondence characteristics of the labeled RSS and its coordinates. Thus, it can improve the accuracy of radio map with limited labeled RSS data. The extensive experiments demonstrate that the proposed method can construct an accurate radio map at a low manual cost, as well as achieve a high localization accuracy.

Key words:  wireless local area network (WLAN), indoor fingerprinting localization, global geometry of manifold structure, semi-supervised manifold alignment, radio map construction