Aroma based localization in GNSS-denied environments

Saiful Islam, Elena-Simona Lohan, Philipp Müller, Mohammad Zahidul Hasan Bhuiyan

Tutkimustuotos: KonferenssiartikkeliProfessional

3 Lataukset (Pure)

Abstrakti

This paper studies infrastructure less localization solutions using aroma fingerprints. These fingerprints are collected under varying conditions from different indoor locations using Ion Mobility Spectrometry based Electronic Noses. A supervised machine learning algorithm for data processing location estimation is proposed. The non-parametric system is trained with data from all locations, and its performance evaluated using data from the same locations collected under different environmental conditions. Five different classifiers are studied and tested for location estimation. The Stochastic Gradient Descent classifier achieved the highest accuracy, with the 푘NN with Euclidian distance also performing reliably under different conditions.
AlkuperäiskieliEnglanti
OtsikkoProceedings of XXXV Finnish URSI Convention on Radio Science
KustantajaURSI
Sivumäärä4
TilaJulkaistu - lokak. 2019
OKM-julkaisutyyppiD3 Artikkeli ammatillisessa konferenssijulkaisussa
TapahtumaFinnish URSI Convention on Radio Science -
Kesto: 1 tammik. 1900 → …

Conference

ConferenceFinnish URSI Convention on Radio Science
Ajanjakso1/01/00 → …

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