Integrated Classification and Localization of Targets Using Bayesian Framework In Automotive Radars

Start Page
4060
End Page
4064
Citation
IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2021)
Contribution to Conference
IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2021 
Publisher DOI
10.1109/ICASSP39728.2021.9414131
Scopus ID
2-s2.0-85115152918
Peer Reviewed
true
Automatic radar based classification of automotive targets, such as pedestrians and cyclist, poses several challenges due to low inter-class variations among different classes and large intra-class variations. Further, different targets required to track in typical automotive scenario can have completely varying dynamics which gets challenging for tracker using conventional state vectors. Compared to state-of-the-art using independent classification and tracking, in this paper, we propose an integrated tracker and classifier leading to a novel Bayesian framework. The tracker’s state vector in the proposed framework not only includes the localization parameters of the targets but is also augmented with the targets’s feature embedding vector. In consequence, the tracker’s performance is optimized due to a better separability of the targets. Furthermore, the classifier’s performance is enhanced due to Bayesian formulation utilizing the temporal smoothing of classifier’s embedding vector.
Schlagworte
Location awareness, Target tracking, Smoothing methods, Radar tracking, Kalman filters, Vehicle dynamics
DDC Class
000: Allgemeines, Wissenschaft