  Automotive radar sensors output a lot of unwanted clutter or ghost
detections, whose position and velocity do not correspond to any real object in
the sensor's field of view. This poses a substantial challenge for environment
perception methods like object detection or tracking. Especially problematic
are clutter detections that occur in groups or at similar locations in multiple
consecutive measurements. In this paper, a new algorithm for identifying such
erroneous detections is presented. It is mainly based on the modeling of
specific commonly occurring wave propagation paths that lead to clutter. In
particular, the three effects explicitly covered are reflections at the
underbody of a car or truck, signals traveling back and forth between the
vehicle on which the sensor is mounted and another object, and multipath
propagation via specular reflection. The latter often occurs near guardrails,
concrete walls or similar reflective surfaces. Each of these effects is
described both theoretically and regarding a method for identifying the
corresponding clutter detections. Identification is done by analyzing
detections generated from a single sensor measurement only. The final algorithm
is evaluated on recordings of real extra-urban traffic. For labeling, a
semi-automatic process is employed. The results are promising, both in terms of
performance and regarding the very low execution time. Typically, a large part
of clutter is found, while only a small ratio of detections corresponding to
real objects are falsely classified by the algorithm.
