The random hypersurface model is well-suited to describe extended target contours. Its applicability is limited only by the mild assumption that the target contour has to be star convex. Gaussian processes provide a sound way to estimate the contour functions, and the ability to model the contour uncertainty in a detailed way at different contour points. However, the association of measurements to target contour points is not optimal in current implementations using Gaussian Processes and the random hypersurface model. In this work, we provide an improved approach compared to the standard approach. The standard approach projects measurements radially onto the predicted contour. Our approach provides expected measurements matching the physical reality of the measurement process more closely. In addition, we perform the association of the whole batch of measurements to the expected contour measurements at once. Compared to a sequential association of individual measurements, this leads to a better association decision.