Robust Wi-Fi based indoor positioning with ensemble learning

This paper proposes a new Wi-Fi based indoor positioning method that is robust over unstable Wi-Fi access points (APs). Because Wi-Fi based indoor positioning relies on unstable and uncontrollable infrastructure (Wi-Fi APs), the positioning performance significantly decreases when such unstable APs are included in the localization system. This paper proposes a indoor positioning method by employing ensemble of weak position estimators, which permits us to construct a robust positioning model. Our proposed boosted position estimator has the following features. 1) The estimator does not overfit the training data and thus it is robust over unstable signals from APs. 2) Because each weak estimator employs a small number of APs for positioning, the estimator is not affected by the curse of dimensionality. 3) Our model can adaptively change the weight (importance) of each weak estimator according to a user´s position in order to achieve a position-aware precise localization.