Augmented CWT Features for Deep Learning-Based Indoor Localization Using WiFi RSSI Data
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Date
2021
Journal Title
Journal ISSN
Volume Title
Publisher
Applied Sciences
Abstract
Localization is one of the current challenges in indoor navigation research. The conventional
global positioning system (GPS) is affected by weak signal strengths due to high levels of signal
interference and fading in indoor environments. Therefore, new positioning solutions tailored for
indoor environments need to be developed. In this paper, we propose a deep learning approach for
indoor localization. However, the performance of a deep learning system depends on the quality
of the feature representation. This paper introduces two novel feature set extractions based on the
continuous wavelet transforms (CWT) of the received signal strength indicators’ (RSSI) data. The
two novel CWT feature sets were augmented with additive white Gaussian noise. The first feature
set is CWT image-based, and the second is composed of the CWT PSD numerical data that were
dimensionally equalized using principal component analysis (PCA). These proposed image and
numerical data feature sets were both evaluated using CNN and ANN models with the goal of
identifying the room that the human subject was in and estimating the precise location of the human
subject in that particular room. Extensive experiments were conducted to generate the proposed
augmented CWT feature set and numerical CWT PSD feature set using two analyzing functions,
namely, Morlet and Morse. For validation purposes, the performance of the two proposed feature sets
were compared with each other and other existing feature set formulations. The accuracy, precision
and recall results show that the proposed feature sets performed better than the conventional feature
sets used to validate the study. Similarly, the mean localization error generated by the proposed
feature set predictions was less than those of the conventional feature sets used in indoor localization.
More particularly, the proposed augmented CWT-image feature set outperformed the augmented
CWT-PSD numerical feature set. The results also show that the Morse-based feature sets trained with
CNN produced the best indoor positioning results compared to all Morlet and ANN-based feature
set formulations.
Description
Keywords
Indoor localization, Data augmentation, Continuous wavelet transform, Deep learning
Citation
Ssekidde, P.; Steven Eyobu, O.; Han, D.S.; Oyana, T.J. Augmented CWT Features for Deep Learning- Based Indoor Localization Using WiFi RSSI Data. Appl. Sci. 2021, 11, 1806. https://doi.org/10.3390/app11041806