Decision-theoretic Sensing

Decision-Theoretic Sensing is a computational framework within active perception that formally models sensor deployment and data acquisition as an optimal decision-making process under uncertainty, distinguishing itself by integrating probabilistic beliefs, action outcomes, and utility maximization to guide sensing strategies.

20

Publications

1.1K

Citations

53

Authors

25

Institutions

Publications per year

2017–2026

1

Authors

53

  1. LC
    Lawrence Carin

    Duke University, United States

    2 publications · 126 citations

    2

    h-index

  2. MH
    Meilin He

    University at Buffalo, State University of New York, United States

    1 publications · 40 citations

    1

    h-index

  3. LM
    Leslie M. Collins

    Duke University, United States

    1 publications · 78 citations

    1

    h-index

  4. RC
    Rebecca Castaño

    Jet Propulsion Laboratory, United States

    1 publications · 45 citations

    1

    h-index

  5. HC
    Han‐Lim Choi

    Korea Advanced Institute of Science and Technology, South Korea

    1 publications · 15 citations

    1

    h-index

1–5 of 53

Venues

  1. 2

    h-index

  2. 1

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  3. IEEE Transactions on Signal Processing

    1 publications · 48 citations

    1

    h-index

  4. Optimization Letters

    1 publications · 15 citations

    1

    h-index

  5. Foundations and Trends in Robotics

    1 publications · 60 citations

    1

    h-index

1–5