# Fingerprints of the Automotive Radar Scattering of Passenger Cars and Vans
## 1 Introduction  
Precise and robust environmental perception and tracking algorithms are essential sensing processes enabling automated driving functions. Both require information about the spatial extent of objects for detection, classification and tracking. With the improving resolution of modern radar sensors, their contribution towards automated driving functions increases significantly [1].  
To accelerate the design and development cycles and to cope with the tremendous requirements regarding safety assurance, virtual test methods and high-fidelity sensor models are needed [2; 3]. Simulating the complex signatures of extended targets is particularly challenging due to the underlying richness of the physical effects. The sensor receives superposed backscatter from multiple reflection centers on the target, leading to temporal and spatial fluctuations that depend on the range resolution and the angular resolution of the sensor [4]. Therefore, the extraction of scattering centers is a widely addressed and well-studied research field. [PERSON] et al. extract virtual scattering centers of vehicles by using ray-tracing simulations [5]. Another way to analyze the backscatter of objects is to perform systematic measurements, e.g., by rotating the sensor around the object [6] or by placing the object under test on a turntable [7]. [PERSON] et al. [8] use synthetic aperture radar (SAR) to extract the location of scattering centers. For passenger cars, most reflections are detected along their contour [9]. Main scattering centers are located at the wheels, light units, pillars, outside mirrors and the license plate [6; 8]. Multipath propagation leads to indirect detections of scattering centers, caused by reflections via the road surface. Altogether, the entity of scattering effects shapes scattering characteristics that can then be integrated into target models [10; 11; 12; 13; 14].  
Until today, there are only a few reports in the literature that analyze the radar scattering fingerprints of different vehicles. [PERSON] et al. [15] show that the body shape of vehicles has a major influence on the respective radar cross section (RCS) value and the spatial location of the detection with the highest RCS value per measurement cycle. However, they do not analyze the accumulated distributions of all received target detections for the different vehicles. When vehicles are observed under certain aspect angles, many detections spread across the entire bounding box of a vehicle [16]. This raises the question if multipath propagation causes additional detections, e.g., at the underbody. Consequently, this might result in different scattering characteristics for different types of vehicles, depending on the geometry of the respective underbody.  
Therefore, this paper analyzes the influence of underbody detections and investigates whether the underbody detections enable radar-based fingerprinting of different vehicle types. Section 2 briefly describes the experimental setup for the conducted test campaign. In Section 3.1, the contribution of underbody detections to the scattering characteristics of vehicles is analyzed using microwave absorbers. Section 3.2 presents the measurement results for six different vehicles: First, for each vehicle, the spatial distribution of detections as well as the RCS distribution are analyzed and their correlation with the geometry of the respective underbody is discussed. Then, the arithmetic mean numbers of target detections per measurement cycle are compared for variable distances between the sensor and the target. Finally, the reflectivity of the different vehicle underbodies is directly compared by visualizing the distribution of detections and their RCS values in longitudinal direction.