Toward Seamless Localization: Situational Awareness Using UWB Wearable Systems and Convolutional Neural Networks

Abstract

Depending on the environment, an increasing number of localization methods are available ranging from satellite-based localization to visual navigation, each with its own advantages and disadvantages. Fast and reliable identification of the environment characteristics is crucial for selecting the best available localization method. This research introduces a deep-learning-based method utilizing data collected with wearable ultra-wideband devices. A novel approach mimicking radar behavior is presented to collect the relevant data. Channel state information is proposed for training of the neural network and enabling the environment detection to obtain the desired situational awareness. The proposed detection approach is evaluated in three types of environments: 1) indoor, 2) open outdoor, and 3) crowded urban. The results show that fast and accurate environment detection for seamless localization purposes can be achieved with a precision of 91% for general scenarios and a precision of 96% for specific use cases.