Name
Abstract | ||
|---|---|---|
Fingerprinting is the prevailing positioning method for location based service (LBS) and indoor positioning applications when compared with other methods such as cell of origin (CoO) and trilateration. It is especially more suitable for complicated indoor environments. However, higher positioning accuracy is still expected for it to match the capabilities of other mature techniques such as GPS. This paper presents a new algorithm for improving the positioning accuracy of the Nearest Neighbour (NN) algorithm from a Wi-Fi-based fingerprinting method. The new algorithm initially used the NN algorithm to identify the initial position estimate of the user being tracked. Then two distance correction values in two roughly perpendicular directions were calculated by the path loss model based on the two signal strength indicator (RSSI) values observed. The errors from the path loss model were eliminated through differencing two calculated distances which were derived from a similar environment. The new algorithm was tested and the results evaluated against that of the NN algorithm. The preliminary results from 24 test points showed that the positioning accuracy of the new approach has improved consistently and the root mean square accuracy improved to 3.4 m from 3.8 m with the NN algorithm. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/DSAA.2014.7058060 | 2014 INTERNATIONAL CONFERENCE ON DATA SCIENCE AND ADVANCED ANALYTICS (DSAA) |
Keywords | DocType | Citations |
positioning algorithm, Fingerprinting, Indoor positioning, Wi-Fi, LBS | Conference | 1 |
PageRank | References | Authors |
0.43 | 5 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yuntian Brian Bai | 1 | 14 | 2.96 |
| Mani Williams | 2 | 2 | 1.46 |
| Falin Wu | 3 | 5 | 4.60 |
| Allison Kealy | 4 | 70 | 12.14 |
| Kefei Zhang | 5 | 52 | 18.45 |