Pedestrian Collision Mitigation Systems (PCMS) are already in the market for some years. Due to continuously evolving EuroNCAP regulations their presence will increase. Visual sensors, already capable of pedestrian classification, provide functional benefits, because the reaction behavior can be optimized when the imminent collision object is recognized as pedestrian or cyclist. Nevertheless their performance will suffer under adverse environmental conditions like darkness, fog, rain or backlight. Even in such unfavorable situations the performance of radar sensors is not significantly deteriorated. Enabling classification capability to automotive radar will further improve road safety and will lower PCMS's overall costs. In this paper, a multi-reflection-point pedestrian target model based on motion analysis is presented. Together with an appropriate sensor model, pedestrian radar signal responses can be provided for a wide range of accident scenarios. Additionally velocity separation requirements that are needed for classification of pedestrians are derived from the simulations. Besides determination of classification features, the model discloses the limits of classical radar signal processing and further offers the opportunity to evaluate parametric spectral analysis. Based on simulated and measured baseband radar signals of pedestrians one of these techniques is deeper analyzed and its enhancement especially on the velocity separation capability is evaluated.