Deep Learning For Localization

Deep Learning for Localization is a methodological framework within indoor localization that employs deep neural networks to learn complex spatial representations from raw sensor data, distinguishing itself by automatically extracting hierarchical features for precise position estimation without relying on hand-crafted models.

705

Publications

53.1K

Citations

2.6K

Authors

755

Institutions

Publications per year

2017–2026

652

Authors

2.6K

  1. XW
    Xuyu Wang

    Auburn University, United States

    11 publications · 2.4K citations

    11

    h-index

  2. SM
    Shiwen Mao

    Auburn University, United States

    11 publications · 2.4K citations

    11

    h-index

  3. MY
    Moustafa Youssef

    Egypt-Japan University of Science and Technology, Egypt

    10 publications · 706 citations

    10

    h-index

  4. HR
    Hamada Rizk

    Tanta University, Egypt

    10 publications · 707 citations

    10

    h-index

  5. AM
    Andrew Markham

    University of Oxford, United Kingdom

    8 publications · 732 citations

    8

    h-index

1–5 of 2.6K

Venues

  1. IEEE Access

    36 publications · 2.1K citations

    24

    h-index

  2. Sensors

    33 publications · 1.3K citations

    23

    h-index

  3. IEEE Sensors Journal

    24 publications · 1.1K citations

    20

    h-index

  4. arXiv (Cornell University)

    Repository · 41 publications · 2K citations

    20

    h-index

  5. IEEE Robotics and Automation Letters

    15 publications · 789 citations

    14

    h-index

1–5