Combination of mmWave imaging and communications for simultaneous localization and mapping

In this thesis, the synergy between millimeter-wave (mmWave) imaging and wireless communications is used to achieve high accuracy user localization and mapping (SLAM) mobile users in an uncharted environment. Such capability is enabled by taking advantage of the high-resolution image of both line-of-sight (LoS) and non-line-of-sight (NLoS) objects that mmWave imaging provides, and by utilizing angle of arrival (AoA) and time of arrival (ToA) estimators from communications. The motivations of this work are as follows: first, enable accurate SLAM from a single viewpoint i.e., using only one antenna array at the base station without any prior knowledge of the environment. The second motivation is the ability to localize in NLoS-only scenarios where the user signal may experience more than one reflection until it reaches the base station. As such, this proposed work will not make any assumptions on what region the user is and will use mmWave imaging techniques that will work for both near and far field region of the base station and account for the scattering properties of mmWave. Similarly, a near field signal model is developed to correctly estimate the AoA regardless of the user location.

This SLAM approach is enabled by reconstructing the mmWave image of the environment as seen by the base station. Then, an uplink pilot signal from the user is used to estimate both AoA and ToA of the dominant channel paths. Finally, AoA/ToA information is projected into the mmWave image to fully localize the user. Simulations using full-wave electromagnetic solvers are carried out to emulate an environment both in the near and far field. Then, to validate, an experiment carried in laboratory by creating a simple two-dimensional scenario in the 220-300 GHz range using a synthesized 13-cm linear antenna array formed by using vector network analyzer extenders and a one-dimensional linear motorized stage that replicates the base station. After taking measurements, this method successfully reconstructs the image of the environment and localize the user position with centimeter accuracy.