In the last two decades, radar-based contactless vital signs monitoring (heartbeat and respiration rate) has raised increasing interest as an emerging approach for healthcare and complementary for other more established technologies. Heartbeat and respiration induce only very subtle rhythmic changes in the reflected radar signature, whereas the signals reflected by larger objects in real scenarios and even themovements of body parts of the subjects being monitored are typically larger. Radar reflection paths are multiple and often vary strongly, especially indoors. It is therefore extremely challenging to determine the correct number of targets and to perform concurrent localization and reliable vital signs monitoring on multiple people in real-world environments. The multipaths (ghost signals) from the reflected signal of one individual, combine with the reflected signals and multipaths of other subjects and with clutter, jeopardizing individual vital signs extraction and localization. The main research activities in this thesis aimed to extend the work of a previous master thesis from SISO (single input single output) radar to a SIMO (single input multiple output) radar framework. The core idea is that the usage of multiple receiver channels that SIMO radar provides can enable an additional degree of freedom (the estimation of the angular position) to distinguish real targets from ghost targets due to multipath, hence improving their rejection and cancellation. Simulation results are then generated to compare SISO and SIMO frameworks for recognition of the number of subjects in a given environment, for their localisation, and for the estimation of their vital signs. Unfortunately, due to access limitation caused by the COVID-19 pandemic to the offices of IMEC, Eindhoven, where this thesis work was mostly performed, the initially planned experimental validation with SIMO radar was not possible to perform.