With the development of millimeter-wave radar technology, it has shown great potential in the field of indoor personnel monitoring. This paper focuses on the problem of personnel motion trajectory tracking with millimeter-wave radar in indoor scenarios and designs an adaptive trajectory tracking system (MRATTS) that combines data preprocessing, target detection and multi-target tracking. The target is detected through point cloud data filtering and DBSCAN clustering, and the target tracking is achieved by using Kalman filtering and the target correlation algorithm. The adaptive Kalman filtering method (AKIT) is introduced to dynamically adjust the noise covariance matrix of the Kalman filter, improving the accuracy and robustness of the system trajectory tracking. Experiments show that the proposed method can effectively process millimeter-wave radar point cloud data, accurately track the trajectories of indoor personnel, and perform well in terms of position error, trajectory smoothness and real-time performance, laying the foundation for applications such as indoor personnel behavior analysis.