Machine Learning For Indoor Localization

While GPS is the de-facto solution for outdoor positioning with a clear sky view, there is no prevailing technology for GPS-deprived areas, including dense city centers, urban canyons, buildings and other covered structures, and subterranean facilities such as underground mines, where GPS signals are severely attenuated or totally blocked. As an alternative to GPS for the outdoors, indoor localization using machine learning is an emerging embedded and Internet of Things (IoT) application domain that is poised to reinvent the way we navigate in various indoor environments. This book discusses advances in the applications of machine learning that enable the localization and navigation of humans, robots, and vehicles in GPS-deficient environments.

This book covers recent advances of machine learning techniques in a broad range of applications in smart cities, automated industry, and emerging businesses.

Implement popular deep learning techniques to make your IoT applications smarter

Unleash the power of TensorFlow and PyTorch to build and train Neural Networks effectively