Indoor Environment-Adaptive Mapping With Beamsteering Massive Arrays

Beamsteering massive arrays have been recently proposed for indoor environment mapping in next 5G scenarios, thanks to their capability to better penetrate materials with respect to current laser or vision-based systems. From the perspective of integrating radars in small portable devices, architectures based on non-coherent processing of raw measurements represent a viable solution to overcome the limitations of current indoor radio mapping techniques, which entail a too high processing or receiver complexity. In this correspondence, we investigate the capability of low-complexity mobile radars, equipped with mm-wave massive arrays, to adapt to the environment in order to reconstruct it, by adjusting a threshold with respect to the collected data and the radiation pattern. Results, corroborated by means of a measurement campaign, show the effectiveness of the proposed approach.