Feature Extraction Based on Manifold Learning for Radio Fingerprint

Qiaolin Pu, Tianshu Tang, Joseph K Y NG, Fawen Zhang

Research output: Chapter in book/report/conference proceedingConference contributionpeer-review

Abstract

Wireless Local Area Network (WLAN) fingerprinting has been extensively studied for indoor localization due to the pervasive facilities. Conventional fingerprint database is composed of a set of raw Received Signal Strength (RSS) which is not processed to features. Even though it provides adequate results in some cases, but for large-scale environment, it brings the storage problem and computational complexity due to the high dimensionality. To address these problems, this paper presents a feature extraction algorithm using a manifold learning called T-distributed Stochastic Neighbor Embedding (TSNE) which extracts these non-linear fingerprint features and reduces the dimensionality simultaneously at offline stage. Then to increase positioning accuracy, out-of-sample extension method is proposed to process the online record to achieve the same dimensionality as the reduced offline database. Furthermore, when facing the major bottleneck of dimensionality reduction (DR) technologies that determining the proper value of dimensionality, we utilize intrinsic dimensionality estimation method to obtain the best dimensionality previously. Experiments are conducted in an actual indoor large-scale environment, and the results demonstrate our approach performs perfectly which reduces the original dimensionality 168 to 10 and achieves better position accuracy simultaneously.

Original languageEnglish
Title of host publication2019 11th International Conference on Wireless Communications and Signal Processing, WCSP 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728135557
DOIs
Publication statusPublished - Oct 2019
Event11th International Conference on Wireless Communications and Signal Processing, WCSP 2019 - Xi'an, China
Duration: 23 Oct 201925 Oct 2019

Publication series

Name2019 11th International Conference on Wireless Communications and Signal Processing, WCSP 2019

Conference

Conference11th International Conference on Wireless Communications and Signal Processing, WCSP 2019
Country/TerritoryChina
CityXi'an
Period23/10/1925/10/19

Scopus Subject Areas

  • Computer Networks and Communications
  • Hardware and Architecture
  • Signal Processing
  • Safety, Risk, Reliability and Quality
  • Artificial Intelligence

User-Defined Keywords

  • Feature Extraction
  • Intrinsic Dimensionality
  • Out-of-sample Extension
  • TSNE
  • WLAN

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