Probabilistic Sensor Modeling

Probabilistic Sensor Modeling is a formal framework that characterizes sensor behavior through probability distributions to represent uncertainty in measurement processes, distinguishing itself from deterministic models by explicitly accounting for stochastic errors and environmental variability.

140

Publications

8K

Citations

364

Authors

189

Institutions

Publications per year

2017–2026

33

Authors

364

  1. WB
    Wolfram Burgard

    University of Freiburg, Germany

    3 publications · 183 citations

    3

    h-index

  2. VI
    Volkan Isler

    University of Minnesota, United States

    3 publications · 189 citations

    3

    h-index

  3. SV
    Sunil Vadera

    University of Salford, United Kingdom

    3 publications · 83 citations

    3

    h-index

  4. LE
    Luis Enrique Sucar

    National Institute of Astrophysics, Optics and Electronics, Mexico

    3 publications · 83 citations

    3

    h-index

  5. JM
    Jörg Müller

    Technische Universität Berlin, Germany

    2 publications · 51 citations

    2

    h-index

1–5 of 364

Venues

  1. IEEE Sensors Journal

    4 publications · 114 citations

    4

    h-index

  2. Mechanical Systems and Signal Processing

    MSSP · 3 publications · 108 citations

    3

    h-index

  3. Sensors

    3 publications · 128 citations

    3

    h-index

  4. Ad Hoc Networks

    3 publications · 81 citations

    3

    h-index

  5. Automatica

    2 publications · 518 citations

    2

    h-index

1–5