A Novel RSSI Fingerprint Positioning Method Based on Virtual AP and Convolutional Neural Network
Shixun Wu, Wenxian Huang, Min Li, Kai Xu
Abstract
Indoor Wi-Fi positioning based on Received Signal Strength Indicator (RSSI) has been widely used in all kinds of location-based services. However, the accuracy of positioning is susceptible to the sharp fluctuation of RSSI and the low sampling density. To address these difficulties, this paper proposes a novel fingerprint positioning method combining the virtual Access Point (AP) technique with Convolutional Neural Network (CNN) classification model. In our proposed method, a data augmentation method is designed to improve the positioning accuracy with few samples. The virtual AP location obtained by the distance ratio positioning method is utilized to adaptively modify the inputs of the CNN model. This new CNN model is used to determine the area of the samples, whereas a virtual AP method computes the accurate position of the samples. The experimental results show that the 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.