Open AccessC‐CNNLoc: Constrained CNN for robust indoor localization with building boundary
Author(s) -
Oh Y.,
Noh H.M.,
Shin W.
Publication year - 2021
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
eISSN - 1350-911X
pISSN - 0013-5194
DOI - 10.1049/ell2.12142
Subject(s) - computer science , convolutional neural network , outlier , boundary (topology) , function (biology) , artificial intelligence , term (time) , building model , pattern recognition (psychology) , computer vision , algorithm , data mining , simulation , mathematics , mathematical analysis , physics , quantum mechanics , evolutionary biology , biology
To enable accurate and reliable indoor localization in a multi‐building environment, a novel constrained convolutional neural network (CNN)‐based indoor localization system (C‐CNNLoc) is proposed using WiFi fingerprinting approach. The proposed network has a sequential structure that firstly classifies a building, followed by estimating the user's location coordinate within the pre‐detected building. Furthermore, the location accuracy is improved by introducing a new loss function that incorporates a penalty term associated with the building boundary. Experimental results illustrate that the proposed method outperforms the existing solutions on the average distance error. The gain comes from that the approach tailored to a multi‐building indoor localization with the sequential structure is prone to successfully correct outliers, that is, predicted location coordinates that lie outside the buildings.