Cloud-based Positioning Method with Visualized Signal Images

This paper presents a cloud-based positioning method that leverages visualized signal images through visual analytics and deep learning. At a mobile client, such as a smart phone, this approach transforms multidimensional signals captured at a known location and at a given time into a signal image and transmits such visual signal images to the Cloud. By collecting and storing many such visual signal images as fingerprints, we can build a visual signal image cloud and produce a signal image map for the geographical region of interest and utilize such signal image map to serve the positioning requests of mobile clients on the move. When a user Alice wants to know her current position, her mobile client will generate a signal image from the multiple signals it receives with timestamp and send this query image to a Cloud server. The server searches the existing signal images stored in the cloud to find those that are similar to this query signal image and estimates the positioning of Alice based on the locations of those similar signal images collected by the server. We evaluate our visual signal images based positioning system on the entire two floors of a large department store and on the street and shops outside the department store, with the signal images collected over 30 minutes before serving positioning queries. The mean error of up to 4 meters is observed. To further verify the applicability of the proposed method, extensive experiments were conducted to distinguish whether a user is indoor or outdoor by applying a deep learning algorithm with 60% of signal images collected for training and 40% signal images for testing. This experiment shows that the proposed method is able to distinguish indoor and outdoor with accuracy of about 95%.