A feature-scaling-based k-nearest neighbor algorithm for indoor positioning systems
文献类型：期刊论文
| 作者 | Li, Dong1; Zhang, Baoxian1,2; Li, Cheng3 |
| 刊名 | Ieee internet of things journal
 |
| 出版日期 | 2016-08-01 |
| 卷号 | 3期号:4页码:590-597 |
| 关键词 | Feature scaling (fs) Fingerprint-based localization Indoor positioning system K-nearest neighbor (knn) |
| ISSN号 | 2327-4662 |
| DOI | 10.1109/jiot.2015.2495229 |
| 通讯作者 | Li, dong(lidong10b@mails.ucas.ac.cn) ; Zhang, baoxian(bxzhang@ucas.ac.cn) ; Li, cheng(licheng@mun.ca) |
| 英文摘要 | With the increasing popularity of wlan infrastructure, wifi fingerprint-based indoor positioning systems have received considerable attention recently. much existing work in this aspect adopts classification techniques that match a vector of radio signal strengths (rsss) reported by a mobile station (ms) to pretrained reference fingerprints sampled from different access points (aps) at different reference points (rps) with known positions. however, in the calculation of signal distances between different rss vectors, existing techniques fail to consider the fact that equal rss differences at different rss levels may not mean equal differences in geometrical distances in complex indoor environment. to address this issue, in this paper, we propose a feature-scaling-based k-nearest neighbor (fs-knn) algorithm for achieving improved localization accuracy. in fs-knn, we build a novel rss-level-based fs model, which introduces rss-level-based scaling weights in the computation of effective signal distances between signal vector reported by a ms and reference fingerprints in a radio map. experimental results show that fs-knn can achieve an average location error as low as 1.70 m, which is superior to existing work. |
| WOS研究方向 | Computer Science ; Engineering ; Telecommunications |
| WOS类目 | Computer Science, Information Systems ; Engineering, Electrical & Electronic ; Telecommunications |
| 语种 | 英语 |
| WOS记录号 | WOS:000381470400016 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| URI标识 | http://www.irgrid.ac.cn/handle/1471x/2376341 |
| 专题 | 中国科学院大学 |
| 通讯作者 | Li, Dong; Zhang, Baoxian; Li, Cheng |
| 作者单位 | 1.Univ Chinese Acad Sci, Res Ctr Ubiquitous Sensor Networks, Beijing 100049, Peoples R China 2.Jiangsu Internet Of Things Res & Dev Ctr, Wuxi 214135, Peoples R China 3.Mem Univ, Fac Engn & Appl Sci, St John, NF A1B 3X5, Canada |
| 推荐引用方式 GB/T 7714 | Li, Dong,Zhang, Baoxian,Li, Cheng. A feature-scaling-based k-nearest neighbor algorithm for indoor positioning systems[J]. Ieee internet of things journal,2016,3(4):590-597. |
| APA | Li, Dong,Zhang, Baoxian,&Li, Cheng.(2016).A feature-scaling-based k-nearest neighbor algorithm for indoor positioning systems.Ieee internet of things journal,3(4),590-597. |
| MLA | Li, Dong,et al."A feature-scaling-based k-nearest neighbor algorithm for indoor positioning systems".Ieee internet of things journal 3.4(2016):590-597. |
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来源：中国科学院大学
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