Aroma based localization in GNSS-denied environments

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.