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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.