Name
Abstract | ||
|---|---|---|
In this paper, we investigate machine learning approaches addressing the problem of geolocation. First, we review some classical learning methods to build a radio map. These methods are split in two categories, which we refer to as likelihood-based methods and fingerprinting methods. Then, we provide a novel geolocation approach in each of these two categories. The first proposed technique relies on a semi-parametric Nadaraya–Watson (NW) estimator of the likelihood, followed by a maximum a posteriori (MAP) estimator of the object’s position. The second technique consists in learning a proper metric on the dataset, constructed by means of a Gradient boosting regressor: a k-nearest neighbor algorithm is then used to estimate the position. The proposed methods are compared on two data sets originated from Sigfox network, and an indoor dataset performed in a three-story building. Experiments show the interest of the proposed methods, both in terms of location estimation performance, and ability to build radio maps. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1016/j.pmcj.2020.101199 | Pervasive and Mobile Computing |
Keywords | DocType | Volume |
Geolocation,Maximum likelihood,Metric learning,RSSI | Journal | 67 |
ISSN | Citations | PageRank |
1574-1192 | 1 | 0.36 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kevin Elgui | 1 | 1 | 0.36 |
| Pascal Bianchi | 2 | 1 | 0.36 |
| François Portier | 3 | 1 | 0.36 |
| Olivier Isson | 4 | 1 | 1.03 |