Performing joint tracking and classification is the ultimate goal for many radar-based applications. For example, in indoor monitoring scenario, it is important to know the target's position as well as the related activities performed by that target. However, the literature often treats the joint problem independently due to its complexity. As a consequence, the dependencies and requirements between the tracking and recognition system are neglected. The main objective of this thesis work is to build two connectable systems for tracking and classifying human activities with radar sensors and address the problems caused by the mutual requirements through system designs. To achieve it, this thesis work proposed a multiple target tracking (MTT) system and a human activity recognition (HAR) system based on a distributed IR-UWB radar sensor network. The result shows that the MTT system is able to track multiple extended targets and extract the Doppler characteristics, and the HAR system provides an end-to-end solution for data fusion and activity classification. In summary, this work provides a foundation for their combination and shows improvements in both tracking and classification compared to the state-of-the-art.