Indoor WLAN localization using high-dimensional manifold alignment with limited calibration load

With the rapid development of Wireless Local Area Network (WLAN) technique, the indoor WLAN localization has caught significant attention. In this paper, a novel indoor WLAN localization approach by using the high-dimensional manifold alignment with limited calibration load is proposed. Different from the conventional dimension-reduction based manifold alignment approach which preserves a limited part of the Received Signal Strength (RSS) data information, we first construct an innovative objective function from the augmented physical locations and the corresponding RSS data. Second, the closed-form solution to the objective function is obtained by applying the Lagrange multiplier approach. Finally, the target location is estimated at the closest point in the manifold. Furthermore, we present some preliminary analysis towards the generalization of the proposed objective function to the scenario with multiple types of measurements used for the localization. The extensive analytical and experimental results demonstrate that the performance of the proposed approach is well with limited calibration load and can be further improved by using more calibrated locations with known RSS data.