Name
Abstract | ||
|---|---|---|
Indoor positioning using location fingerprints, which are received signal strength (RSS) from wireless access points (APs), has become a hot research topic during the last a few years. Traditional pattern classification based fingerprinting localization methods suffer high computational burden and require a large number of classifiers to determine the object location. To handle this problem, axial-decoupled indoor positioning based on location-fingerprints is proposed in this paper. The purpose is to reduce the decision complexity while keeping localization accuracy through computing the position on X-and Y-axis independently. First, the framework of axial-decoupled indoor positioning using location fingerprints is given. Then, the training and decision process of the proposed axial-decoupled indoor positioning is described in detail. Finally, pattern classifiers including the least squares support vector machine (LS-SVM), support vector machine (SVM) and traditional k-nearest neighbors (K-NN) are adopted and embedded in the proposed framework. Experimental results illustrate the effectiveness of the proposed axial-decoupled positioning method. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1007/978-981-10-3002-4_2 | Communications in Computer and Information Science |
Keywords | Field | DocType |
Location fingerprint,Axial-decoupled,Indoor positioning,Pattern classification | Data mining,Wireless,Least squares support vector machine,Computer science,Support vector machine,Signal strength,Decision process,RSS | Conference |
Volume | ISSN | Citations |
662 | 1865-0929 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yanhua Wei | 1 | 0 | 0.34 |
| Yan Zhou | 2 | 0 | 0.34 |
| Dongli Wang | 3 | 0 | 0.68 |
| Xianbing Wang | 4 | 90 | 9.98 |