Location awareness is crucial for numerous emerging wireless indoor applications. Deep
learning algorithms have demonstrated the potential for achieving the required level of …

Recently, deep-learning-based indoor localisation systems have attracted attention owing to
their higher performance compared with traditional indoor localization systems. However, to …

Although fingerprint-based methods could achieve high location accuracy for device-free
indoor localization, it requires cost-expensive massive labeling. In order to fully exploit the …

Deep-learning classifiers can effectively improve the accuracy of fingerprint-based indoor
positioning. During fingerprint database construction, all received signal strength indicators …

Distribution discrepancy is an intrinsic challenge in existing fingerprint-based indoor
positioning system (s)(FIPS) due to real-time environmental variations; thus, the positioning …

Deep learning (DL) application is proven helpful in a vast research field. One recent trend is
to employ DL in radio frequency (RF)-based indoor localization. The fingerprint technique is …

Recent advances in (deep) machine learning offer new opportunities to solve indoor
fingerprint-based localization problems. However, the majority of localization solutions …

Indoor positioning based on machine-learning (ML) models has attracted widespread
interest in the last few years, given its high performance and usability. Supervised …

In recent years, because of the growing demand for location based services in indoor
environment and development of Wi-Fi, fingerprint-based indoor localization has attracted …

Although indoor positioning has long been investigated by various means, its accuracy
remains concern. Several recent studies have applied machine learning algorithms to …