In the Roadmap of 2025, EURO NCAP has announced that it will reward car manufacturers that include Child Presence Detection (CPD) technologies in their vehicles starting from 2022. Additionally, seat belt detectors are currently based on pressure sensors which can be falsely triggered with large objects placed on the seats. By basing the detection of people on human vital signs presence instead, these errors can be avoided. Therefore, in recent years, there has been an increasing need to find a solution for multiple people detection and localization in vehicles for both CPD and seat-belt reminder systems in the automotive industry.

With Ultra Wide-Band (UWB) radar, non-invasive human detection is possible through the identification of vital signs characteristics in the radar data. This work aims to improve existing literature by developing a network of UWB radars to perform multiple people detection and localization.

Specifically, an algorithm for de-centralized vital signs detection is proposed, based on the analysis of a novel model for radar signatures of vital signs. Additionally, a centralized association block is developed to fuse the detections from all radars using machine learning-based cost-matrix computation. The performance of the proposed processing pipeline is tested experimentally with a multistatic radar network. A simulation framework is developed for radar data generation to evaluate the results obtained in the experiments, and to propose variations on the evaluated radar topologies.

It can be concluded that the detection and localization of humans in the environment is possible with the proposed framework, with localization RMSE of 16cm for single and double target scenarios. The distribution of multiple focus points and the introduction of bistatic radars enhances the detection, and thus localization, w.r.t. current methods based on monostatic radars and MIMO radars.