In smart office environments, intelligent control of IoT devices based on human movement and the monitoring of sedentary behavior are crucial. To address object occlusion problem, this thesis proposes an innovative solution: integrating UWB radars into ceiling-mounted office lights, in contrast to conventional wall-mounted setups. The core contribution of this work is the development of an indoor human localization and activity recognition system using ceiling-mounted radars. A novel method is proposed for joint human tracking and activity recognition. The system and the proposed method is validated in a 3.5 m × 6 m office environment with three ceiling-mounted Novelda X4 radars. The experimental results demonstrate high performance: trajectory tracking achieves a root mean square error (RMSE) as low as 0.23 m, representing a 52.6% improvement over the baseline method, while activity classification for walking, standing, and sitting reaches an accuracy of up to 98%. These findings demonstrate the feasibility and effectiveness of ceiling-mounted UWB radars for accurate human tracking and activity recognition in office-like environments.

The increasing use of automotive radars on roads has led to a significant issue of mutual interference between them. To better understand the characteristics and impact of this interference, it is necessary to perform analytical investigations of FMCW radar-to-radar interference. This thesis presents analytical models for FMCW-to-FMCW mutual interference in ideal conditions, categorizing them into four cases based on different parameters: fully synchronous, generally synchronous, periodically asynchronous, and aperiodically asynchronous. The impact of these types of interference is evaluated on the probability of detection and false alarm probability using CFAR.