Augmented CWT Features for Deep Learning-Based Indoor Localization Using WiFi RSSI Data

Localization is one of the current challenges in indoor navigation research. The conventional global positioning system (GPS) affected by weak signal strengths due to high levels interference and fading environments. Therefore, new solutions tailored for environments need be developed. In this paper, we propose a deep learning approach localization. However, performance depends on quality feature representation. This paper introduces two novel set extractions based continuous wavelet transforms (CWT) received strength indicators’ (RSSI) data. CWT sets were augmented with additive white Gaussian noise. first image-based, second composed PSD numerical data that dimensionally equalized using principal component analysis (PCA). These proposed image both evaluated CNN ANN models goal identifying room human subject was estimating precise location particular room. Extensive experiments conducted generate analyzing functions, namely, Morlet Morse. For validation purposes, compared each other existing formulations. accuracy, precision recall results show performed better than used validate study. Similarly, mean localization error generated predictions less those More particularly, CWT-image outperformed CWT-PSD set. also Morse-based trained produced best all ANN-based