scholarly journals An Improved Indoor Positioning Method Based on Affinity Propagation Clustering

2016 ◽  
Author(s):  
Guochuan Deng ◽  
Sujuan Qin
Keyword(s):  
Indoor Positioning ◽  
Affinity Propagation ◽  
Affinity Propagation Clustering ◽  
Positioning Method

scholarly journals An Adaptive Weighted KNN Positioning Method Based on Omnidirectional Fingerprint Database and Twice Affinity Propagation Clustering

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2502 ◽  
Author(s):  
Jingxue Bi ◽  
Yunjia Wang ◽  
Xin Li ◽  
Hongxia Qi ◽  
Hongji Cao ◽  
...  
Keyword(s):  
Nearest Neighbor ◽  
Great Influence ◽  
Weighted Average ◽  
Affinity Propagation ◽  
Cluster Center ◽  
K Nearest Neighbor ◽  
K Value ◽  
Affinity Propagation Clustering ◽  
Positioning Method ◽  
Fingerprint Database

The human body has a great influence on Wi-Fi signal power. A fixed K value leads to localization errors for the K-nearest neighbor (KNN) algorithm. To address these problems, we present an adaptive weighted KNN positioning method based on an omnidirectional fingerprint database (ODFD) and twice affinity propagation clustering. Firstly, an OFPD is proposed to alleviate body’s sheltering impact on signal, which includes position, orientation and the sequence of mean received signal strength (RSS) at each reference point (RP). Secondly, affinity propagation clustering (APC) algorithm is introduced on the offline stage based on the fusion of signal-domain distance and position-domain distance. Finally, adaptive weighted KNN algorithm based on APC is proposed for estimating user’s position during online stage. K initial RPs can be obtained by KNN, then they are clustered by APC algorithm based on their position-domain distances. The most probable sub-cluster is reserved by the comparison of RPs’ number and signal-domain distance between sub-cluster center and the online RSS readings. The weighted average coordinates in the remaining sub-cluster can be estimated. We have implemented the proposed method with the mean error of 2.2 m, the root mean square error of 1.5 m. Experimental results show that our proposed method outperforms traditional fingerprinting methods.

WLAN indoor positioning method based on gradient boosting and particle filtering

2019 ◽  
Vol 14 (6) ◽  
pp. 535
Author(s):  
Libin Hu ◽  
Zhongtao Li ◽  
Xinghai Yang ◽  
Changzhi Wei
Keyword(s):  
Particle Filtering ◽  
Indoor Positioning ◽  
Gradient Boosting ◽  
Positioning Method
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