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This paper describes a framework for precise self-localization using 2D radar scan matching based on a digitalized map. For this purpose, radars, odometers, a gyroscope and a global digital map are combined. Basically estimated ego-motion using motion sensors is improved using a novel scan matching approach in order to attain globally corrected self-localization results. The matching process is based on map information, iterative optimization using the Gauß-Helmert-Model and two novel weighting methods to register the environment map using radar information. This approach focuses on self-localization in a global frame without using Global Navigation Satellite Systems (GNSS). Beside our main innovation of using native non-discretized map lines for matching we also apply two novel weighting methods to cope with noisy radar scans for matching problem. By applying the Gauß-Helmert-Model we also consider the individual measurement uncertainties to make the approach even more robust against noisy data. Using map lines and data points categorizes our approach in the point-to-feature scan matching family. The performance and usability of the proposed approach is evaluated in real-world experiments and compared qualitatively and quantitatively to the state of the art matching methods. The results illustrate an improvement in precision and computational demand compared to other point cloud based matching methods.