This project will develop a reproducible digital-twin pipeline for generating site-specific radio sensing data and evaluating distributed and closed-loop integrated sensing and communication (ISAC) algorithms for 6G networks. The digital twin will represent urban and indoor environments, distributed base stations, antenna configurations, radio propagation, mobile targets, and sensing and communication resource settings. It will generate channel and sensing observations, including complex-valued samples where needed, together with trajectories, detection and association labels, and uncertainty information across many positions, mobility patterns, signal-to-noise ratios, clutter levels, and random seeds. The generated data will support two closely connected research tasks. First, distributed multi-target tracking and target handover will be studied for base stations with partially overlapping fields of view. Factor-graph and belief-propagation methods will be compared with centralized and local baselines in terms of tracking accuracy, track continuity, communication overhead, and computational cost. Second, closed-loop sensing, communication, and control will be studied for mobile entities such as UAVs, robots, and vehicles. Active-inference and related uncertainty-aware methods will jointly estimate system states, select sensing resources, and plan control actions. Learned localization and uncertainty models may be trained using the digital-twin data and embedded in the closed-loop evaluation. Large-scale computing is required because statistically reliable conclusions require extensive parameter sweeps and many independent Monte Carlo realizations. The study will vary scene geometry, base-station deployment, target number and density, channel and noise conditions, sensing bandwidth, planning horizon, and algorithm parameters. Independent scenarios will be executed in parallel through Slurm job arrays, followed by aggregate statistical analysis. Expected outputs are validated research software, reproducible non-sensitive datasets and metadata, calibrated models, and performance and complexity results for scientific publications and future 6G-DISAC experiments. The applicant, Liping Bai, is a doctoral student at the Department of Electrical Engineering, Chalmers University of Technology. Main supervisor: Professor Henk Wymeersch, Department of Electrical Engineering, Chalmers University of Technology.