Data-driven Range Estimation

Data-Driven Range Estimation is a methodological approach within range estimation that fundamentally relies on empirical data and statistical learning techniques to infer or predict range values, distinguishing itself from model-based or physics-driven approaches by prioritizing observed data patterns over theoretical assumptions.

14

Publications

732

Citations

28

Authors

20

Institutions

Publications per year

2017–2026

3

Authors

28

  1. LB
    Leland Best

    Lockheed Martin (United States), United States

    1 publications · 14 citations

    1

    h-index

  2. MC
    Mo–Yuen Chow

    North Carolina State University, United States

    1 publications · 65 citations

    1

    h-index

  3. MM
    Michael Mageo

    Southwest Research Institute, United States

    1 publications · 14 citations

    1

    h-index

  4. JM
    José M. Venzal

    Universidad de la República de Uruguay, Uruguay

    1 publications · 39 citations

    1

    h-index

  5. SS
    Stefan Seeger

    University of Zurich, Switzerland

    1 publications · 17 citations

    1

    h-index

1–5 of 28

Venues

  1. IFAC Proceedings Volumes

    1 publications · 15 citations

    1

    h-index

  2. Neural Computing and Applications

    1 publications · 43 citations

    1

    h-index

  3. IEEE Sensors Journal

    1 publications · 12 citations

    1

    h-index

  4. Pattern Recognition

    1 publications · 14 citations

    1

    h-index

  5. Experimental and Applied Acarology

    1 publications · 39 citations

    1

    h-index

1–5