TR2020-131

Extended Object Tracking with Automotive Radar Using Learned Structural Measurement Model


    •  Xia, Y., Wang, P., Berntorp, K., Boufounos, P.T., Orlik, P.V., Svensson, L., Granstrom, K., "Extended Object Tracking with Automotive Radar Using Learned Structural Measurement Model", IEEE Radar Conference (RadarCon), September 2020.
      BibTeX TR2020-131 PDF
      • @inproceedings{Xia2020sep,
      • author = {Xia, Yuxuan and Wang, Pu and Berntorp, Karl and Boufounos, Petros T. and Orlik, Philip V. and Svensson, Lennart and Granstrom, Karl},
      • title = {Extended Object Tracking with Automotive Radar Using Learned Structural Measurement Model},
      • booktitle = {IEEE Radar Conference (RadarCon)},
      • year = 2020,
      • month = sep,
      • url = {https://www.merl.com/publications/TR2020-131}
      • }
  • MERL Contacts:
  • Research Areas:

    Computational Sensing, Machine Learning, Optimization, Signal Processing

This paper presents a data-driven measurement model for extended object tracking (EOT) with automotive radar. Specifically, the spatial distribution of automotive radar measurements is modeled as a hierarchical truncated Gaussian with structural geometry parameters (e.g., truncation bounds, their orientation, and a scaling factor) learned from the training data. The contribution is twofold. First, the learned measurement model can provide an adequate resemblance to the spatial distribution of real-world automotive radar measurements. Second, large-scale offline training datasets can be leveraged to learn the geometry-related parameters and offload the computationally demanding model parameter estimation from the state update step. The learned structural measurement model is further incorporated into the random matrix-based EOT approach with a new state update step. The effectiveness of the proposed approach is verified on the nuScenes dataset.