A multi-reflection-point target model for classification of pedestrians by automotive radar

Pedestrian Collision Mitigation Systems (PCMS) are in the market for a few years. Due to continuously evolving Euro NCAP regulations their presence will rapidly increase. Visual sensors, already capable of pedestrian classification, provide functional benefits because system responses can be better adapted to expected pedestrian's behavior. Nevertheless their performance will suffer under adverse environmental conditions like darkness, fog, rain or backlight. Even in such situations the performance of radar sensors is not significantly deteriorated. Enabling classification capability for radar-based systems will increase road safety further 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 relevant accident scenarios, without risk for the health of test persons. Besides determination of human classification features, the model provides identification of the limits in classical radar signal processing. Beyond these borderlines it offers the opportunity to evaluate parametric spectral analysis methods.