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A K-Nearest Neighbor Indoor Fingerprint Location Method Based on Coarse Positioning Circular Domain and the Highest Similarity Threshold

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成果类型:
期刊论文
作者:
Li, Xiaonian;Dai, Zhicheng;He, Lamei
通讯作者:
Zhicheng Dai
作者机构:
[He, Lamei; Li, Xiaonian] Longdong Univ, Sch Informat Engn, Qingyang, Gansu, Peoples R China.
[Dai, Zhicheng] Cent China Normal Univ, Fac Artificial Intelligence Educ, Natl Engn Res Ctr E Learning, Wuhan, Hubei, Peoples R China.
通讯机构:
[Zhicheng Dai] N
National Engineering Research Center for E-learning, Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan, Hubei, People's Republic of China
语种:
英文
关键词:
BLE;fingerprint location;coarse positioning;K-nearest neighbor;threshold
期刊:
Measurement Science And Technology
ISSN:
0957-0233
年:
2023
卷:
34
期:
1
页码:
015108
基金类别:
National Natural Science Foundation of China [62277026]
机构署名:
本校为其他机构
院系归属:
国家数字化学习工程技术研究中心
摘要:
There are two problems with traditional indoor fingerprint location methods. First, irrelevant fingerprints in a fingerprint database interfere with the matching phase, which leads to poor positioning accuracy and stability of positioning results, and second, there is a large amount of computational overhead in the matching phase. Therefore, this paper proposes a K-nearest neighbor indoor fingerprint location method based on coarse positioning circular domain and the highest similarity threshold. In this method, a circular domain is formed in a coarse positioning process to narrow the position...

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