Feature-based Data Association

Feature-Based Data Association is a data association method that relies on the comparison of extracted features from sensor measurements to establish correspondences between observations and existing tracks, distinguished from other methods by its dependence on feature-level evidence rather than raw data or spatial proximity alone.

18

Publications

497

Citations

60

Authors

30

Institutions

Publications per year

2017–2026

5

Authors

60

  1. AW
    Adam Watkins

    Duke University, United States

    2 publications · 44 citations

    2

    h-index

  2. JT
    Jonathan T. DeSena

    Johns Hopkins University Applied Physics Laboratory, United States

    2 publications · 44 citations

    2

    h-index

  3. NS
    Nate Strawn

    Duke University, United States

    2 publications · 44 citations

    2

    h-index

  4. EM
    Elizabeth Munch

    Michigan State University, United States

    2 publications · 44 citations

    2

    h-index

  5. DR
    David Rouse

    Cornell University, United States

    2 publications · 44 citations

    2

    h-index

1–5 of 60

Venues

  1. 3

    h-index

  2. PLoS ONE

    1 publications · 25 citations

    1

    h-index

  3. International Conference on Information Fusion

    Conference · 1 publications · 13 citations

    1

    h-index

  4. IEEE Robotics and Automation Letters

    1 publications · 58 citations

    1

    h-index

  5. arXiv (Cornell University)

    Repository · 1 publications · 29 citations

    1

    h-index

1–5