RT Journal Article
T1 A fingerprint-based localization algorithm based on LSTM and data expansion method for sparse samples
A1 Jia, Bing
A1 Qiao, Wenling
A1 Zong, Zhaopeng
A1 Liu, Shuai
A1 Hijji, Mohammad
A1 Del Ser, Javier
A1 Muhammad, Khan
AB The accuracy of WiFi fingerprint-based localization is related to the number of reference points, generally, to obtain better positioning accuracy, enough samples must be collected, which will inevitably lead to a huge sampling workload. Thus, it will be of great significance to design an algorithm using sparse samples to achieve positioning accuracy like that of dense samples. This paper proposes a WiFi fingerprint-based localization algorithm using Long Short-Term Memory Network (LSTM) with explainable feature and a sparse sample expansion algorithm (PGSE) based on Principal component analysis and Gaussian process regression for sparse samples. Specifically, in the case of limited number of collected reference points, principal component analysis is used to select the access point, and Gaussian process regression is used to model the reference point coordinates and the corresponding received signal strength values in the training sample set, to expand the signal data and construct a new fingerprint database. The effectiveness of the PGSE algorithm is verified by using the public dataset ’UJIIndoorLoc’. At the same time, the applicability of PGSE expansion algorithm to data with temporal information is verified in the fingerprint-based localization method. In addition, this paper also proposes a WiFi-RSSI indoor localization method based on Long Short-Term Memory Network. Lots of experiments are conducted in the actual scenes and the results are compared with several existing methods. The results indicate that the proposed method improves the precision of indoor localization on an average level compared to state-of-the-art methods.
SN 0167-739X
YR 2022
FD 2022-12
LA eng
NO Jia , B , Qiao , W , Zong , Z , Liu , S , Hijji , M , Del Ser , J & Muhammad , K 2022 , ' A fingerprint-based localization algorithm based on LSTM and data expansion method for sparse samples ' , Future Generation Computer Systems , vol. 137 , pp. 380-393 . https://doi.org/10.1016/j.future.2022.07.021
NO Publisher Copyright: © 2022 Elsevier B.V.
NO This work was supported by the Government of China . Thanks to the National Natural Science Foundation of China (No. 42161070 , 41761086 and 41871363 ), the Natural Science Foundation of the Inner Mongolia Autonomous Region (No. 2019MS06030 ), the Natural Science Foundation of the Inner Mongolia Autonomous Region (No. 2017JQ09 ) and the Grassland Elite Project of the Inner Mongolia Autonomous Region (No. CYYC5016 ). Javier Del Ser also acknowledges funding support from the Department of Education of the Basque Government (Consolidated Research Group MATHMODE, IT1456-22 ).
DS TECNALIA Publications
RD 28 sept 2024