Advancing Passive Localization via beamsteering in Near-Field ISAC Systems

The integrated sensing and communication (ISAC) paradigm focuses on the interaction between communication parameters and their optimization to improve the sensing performance in sixth-generation cellular systems. Beamsteering is particularly beneficial in communication systems, where highly directional beams provide fine-grained spatial information which are essential for accurate localization and tracking in ISAC systems. This paper investigates passive object localization based on beam pattern searching and selection in 60 GHz frequency band. We design a passive localization algorithm that uses beam information as the main feature, along with machine learning tools for localization. The approach is tested through experimental activities conducted in the considered frequency band, forming multiple extremely narrow beams. These beams can be selectively oriented towards moving subjects or objects in the area or directed to illuminate different regions of the same environment. Initial results presented in the paper are very promising, confirming that high accuracy (up to 99 %) is achievable under controlled monitoring conditions.