| Popis: |
Wireless Positioning Systems (WPS) are popular alternative localization methods, especially in dense urban areas where GPS has known limitations. Map-matching (MM) has been used as an approach to improve the accuracy of the estimated locations of WiFi Access Points (APs), and thus the accuracy of a wireless positioning system. Large-scale wireless positioning differs from satellite based positioning in at least two aspects: First, wireless positioning systems typically derive the location estimates based on war-driving access point (AP) data. Second, the locations of the AP beacons are not generally known at the same precision as that of the satellite locations. This results in lower accuracy and a lower confidence factor in the use of wireless positioning. This paper presents a fast selective look-ahead map-matching technique, called SLAMM. Existing MM algorithms developed for real-time location tracking of a moving vehicle are ill-suited for matching large collections of war-driving data due to the time complexity. Another unique feature of SLAMM is the map-matching of critical location samples in an AP trace to the road network before matching non-critical samples. Our experiments over a real dataset of 70 million AP samples show that SLAMM is accurate and significantly faster than the traditional MM approaches. |
