Military simulation is essential for modern warfare, providing a virtual environment for training, analysis, and rehearsal of procedures. Accurate correlation between simulated entities, called tracks, and their radar detections, called tracks, is crucial for generating reliable data, vital for evaluating military operations and improving training exercises. However, factors like radar noise, communication errors, and simulation inaccuracies complicate this correlation. This thesis aims to develop a robust method for correlating simulated truth entities with corresponding radar tracks in military simulations. The proposed method tackles challenges such as radar noise and communication errors to improve the reliability and validity of simulation statistics. The research encompasses the theoretical development of three correlation algorithms, one of which serves as a benchmark for verification. The methods were evaluated across various simulated scenarios, in which the two correlation methods consistently outperformed the benchmark, particularly in scenarios with fewer data points.