dynamic adaptive model for indoor wlan localization

To support robust indoor localization, it is presented that a dynamic adaptive model (DAM) for WLAN (wireless local area network) location fingerprinting which can provide updated radio maps depending on the real time data from several base stations (BS). The model takes the spatial relationships between the BSs and the sample points of the radio map into account that the data of BSs and radio map is respectively used as the inputs and outputs of multilayer neural networks to update radio maps dynamically. In order to catch tempo-spatial environmental changes, the multivariate outlier detection technique is applied to examine the data of BSs. According to the detecting results, a retraining process and an interpolation method considering the floor plan are used to update the functional model and make the model adapt to tempo-spatial environmental changes. The model is evaluated in indoor dynamic environments. Compared to conventional ones, the average location error of the proposed model-based method decreases more than 10% in time-varying environments; and after spatial environmental changes (radio beacons are moved), its average location error increases 10% to 20% which is much lower than 165% increase of others. Moreover, the localization accuracy is around 3 m, holding the original performance. The results prove the adaptation of the proposed model to the tempo-spatial environmental changes. However, compared to conventional location fingerprinting, the model brings a little more computational overhead.