ADAPTIVE RSSI FINGERPRINT POSITIONING METHOD BASED ON VIRTUAL AP FUSED WITH CNN MODEL

The indoor Wi-Fi positioning system based on RSSI positioning fingerprint has been widely used in all kinds of location-based services. However, the accuracy of fingerprint positioning is susceptible to the sharp fluctuation of RSSI which is difficult to meet the demand for precise indoor positioning. To overcome these difficulties, this paper proposes a new positioning method combining virtual AP technology with a high-precision CNN classification model. In the proposed method, the position of the virtual AP was obtained by distance ratio localization, and this information was fused with RSSI as the input of the data augmented CNN model to determine the position of the sample. Through designing the experimental scheme, we collected the actual user terminal RSSI data, built the dataset of fingerprint positioning, and verified the effectiveness of the positioning fingerprint method. The experiment results on this dataset show that the proposed method's accuracy of area determination is up to 91%, and 95% of the positioning error is controlled within 2 meters. Compared with the existing positioning methods, the proposed method has a significant improvement in terms of positioning accuracy.