TR2020-069

Extended Object Tracking Using Hierarchical Truncation Model With Partial-View Measurements


    •  Xia, Y., Wang, P., Berntorp, K., Mansour, H., Boufounos, P.T., Orlik, P.V., "Extended Object Tracking Using Hierarchical Truncation Model With Partial-View Measurements", IEEE Sensor Array & Multichannel Signal Processing Workshop (SAM), DOI: 10.1109/SAM48682.2020.9104388, June 2020.
      BibTeX TR2020-069 PDF
      • @inproceedings{Xia2020jun,
      • author = {Xia, Yuxuan and Wang, Pu and Berntorp, Karl and Mansour, Hassan and Boufounos, Petros T. and Orlik, Philip V.},
      • title = {Extended Object Tracking Using Hierarchical Truncation Model With Partial-View Measurements},
      • booktitle = {IEEE Sensor Array & Multichannel Signal Processing Workshop (SAM)},
      • year = 2020,
      • month = jun,
      • doi = {10.1109/SAM48682.2020.9104388},
      • url = {https://www.merl.com/publications/TR2020-069}
      • }
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  • Research Areas:

    Computational Sensing, Optimization, Signal Processing

This paper introduces the hierarchical truncated Gaussian model in representing automotive radar measurements for extended object tracking. The model aims at a flexible spatial distribution with adaptive truncation bounds to account for partial-view measurements caused by self-occlusion. Built on a random matrix approach, we propose a new state update step together with an adaptively update of the truncation bounds. This is achieved by introducing spatialdomain pseudo measurements and by aggregating partial-view measurements over consecutive time domain scans. The effectiveness of the proposed algorithm is verified on a synthetic dataset and an independent dataset generated using the MathWorks Automated Driving toolbox.

 

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