3D millimeter-Wave Multi-Target Sensing

This paper addresses the challenge of achieving precise 3D localization of multiple objects in indoor environments using millimeter-wave (mmWave) sensing. mmWave positioning systems have recently emerged as a promising technology offering cm-level accuracy and robustness; however, the radar-like nature of mmWave technology presents challenges in multi-target positioning, particularly in complex environments where distinguishing between multiple objects becomes difficult. To address this, we explore clustering as a solution to analyze data from mmWave sensors and group similar data points, facilitating the identification of distinct targets. This paper aims to leverage the potential of mmWave radar technology to achieve precise ranging and angling measurements in multi-target environments, presenting a comprehensive methodology for evaluating the performance of mmWave sensors for achieving 3D positioning accuracy using four clustering approaches: K-Means, DBSCAN, Affinity Propagation, and BIRCH. The experimental results highlight the potential and challenges of each approach in terms of accuracy, robustness and execution time.