Improvement on velocity estimation of an extended object
Jifeng Ru, Cuichun Xu · 2017
Conventional point object model based automotive radar tracking system assumes at most one detection received from the target object at a time. However, in real applications for an extended object, such as a passenger car, located within a close range to a high-resolution radar or LIDAR, the system usually receives multiple reflections from different parts of the object. This can introduce large bias into a velocity estimation performed by a point object model based tracking system. Doppler-azimuth profile based approach accounts for the cluster of detections and could give a very accurate velocity vector of the extended object. However, depending on the position and orientation of the object, the linear equation set could be ill-conditioned, in which case the estimated velocity will suffer from substantial error. In this paper, we first propose a new approach to estimate the heading of a moving object using principle component analysis based on the detection cluster trajectory. We then propose an approach to fuse the three above-mentioned velocity estimators as each estimator faces challenges in different situations. Road data from a 77GHz radar is used for performance illustration.