PMID- 37837051
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231031
IS  - 1424-8220 (Electronic)
IS  - 1424-8220 (Linking)
VI  - 23
IP  - 19
DP  - 2023 Oct 2
TI  - Indoor Localization Algorithm Based on a High-Order Graph Neural Network.
LID - 10.3390/s23198221 [doi]
LID - 8221
AB  - Given that fingerprint localization methods can be effectively modeled as 
      supervised learning problems, machine learning has been employed for indoor 
      localization tasks based on fingerprint methods. However, it is often challenging 
      for popular machine learning models to effectively capture the unstructured data 
      features inherent in fingerprint data that are generated in diverse propagation 
      environments. In this paper, we propose an indoor localization algorithm based on 
      a high-order graph neural network (HoGNNLoc) to enhance the accuracy of indoor 
      localization and improve localization stability in dynamic environments. The 
      algorithm first designs an adjacency matrix based on the spatial relative 
      locations of access points (APs) to obtain a graph structure; on this basis, a 
      high-order graph neural network is constructed to extract and aggregate the 
      features; finally, the designed fully connected network is used to achieve the 
      regression prediction of the location of the target to be located. The 
      experimental results on our self-built dataset show that the proposed algorithm 
      achieves localization accuracy within 1.29 m at 80% of the cumulative 
      distribution function (CDF) points. The improvements are 59.2%, 51.3%, 36.1%, and 
      22.7% compared to the K-nearest neighbors (KNN), deep neural network (DNN), 
      simple graph convolutional network (SGC), and graph attention network (GAT). 
      Moreover, even with a 30% reduction in fingerprint data, the proposed algorithm 
      exhibits stable localization performance. On a public dataset, our proposed 
      localization algorithm can also show better performance.
FAU - Kang, Xiaofei
AU  - Kang X
AUID- ORCID: 0000-0002-1733-079X
AD  - College of Communication and Information Engineering, Xi'an University of Science 
      and Technology, Xi'an 710054, China.
FAU - Liang, Xian
AU  - Liang X
AD  - College of Communication and Information Engineering, Xi'an University of Science 
      and Technology, Xi'an 710054, China.
FAU - Liang, Qiyue
AU  - Liang Q
AD  - College of Communication and Information Engineering, Xi'an University of Science 
      and Technology, Xi'an 710054, China.
LA  - eng
PT  - Journal Article
DEP - 20231002
PL  - Switzerland
TA  - Sensors (Basel)
JT  - Sensors (Basel, Switzerland)
JID - 101204366
SB  - IM
PMC - PMC10575147
OTO - NOTNLM
OT  - Wi-Fi fingerprint
OT  - adjacency matrix
OT  - graph neural network
OT  - indoor localization
COIS- The authors declare no conflict of interest.
EDAT- 2023/10/14 10:46
MHDA- 2023/10/14 10:47
PMCR- 2023/10/02
CRDT- 2023/10/14 01:21
PHST- 2023/06/25 00:00 [received]
PHST- 2023/09/05 00:00 [revised]
PHST- 2023/09/30 00:00 [accepted]
PHST- 2023/10/14 10:47 [medline]
PHST- 2023/10/14 10:46 [pubmed]
PHST- 2023/10/14 01:21 [entrez]
PHST- 2023/10/02 00:00 [pmc-release]
AID - s23198221 [pii]
AID - sensors-23-08221 [pii]
AID - 10.3390/s23198221 [doi]
PST - epublish
SO  - Sensors (Basel). 2023 Oct 2;23(19):8221. doi: 10.3390/s23198221.