State-space Tracking Models

State-Space Tracking Models is a mathematical framework for dynamic estimation that represents the evolution of a target's state over time using a system of latent state equations and observation equations, distinguished by its explicit modeling of temporal dependencies and uncertainties in both process and measurement domains.

279

Publications

16.4K

Citations

633

Authors

289

Institutions

Publications per year

2017–2026

66

Authors

633

  1. WB
    W.D. Blair

    Naval Surface Warfare Center, United States

    7 publications · 172 citations

    7

    h-index

  2. YB
    Yaakov Bar‐Shalom

    University of Connecticut, United States

    7 publications · 1.6K citations

    7

    h-index

  3. VP
    Vesselin P. Jilkov

    University of New Orleans, United States

    7 publications · 3.1K citations

    7

    h-index

  4. XR
    X. Rong Li

    University of New Orleans, United States

    6 publications · 765 citations

    6

    h-index

  5. JL
    Jian Lan

    Xi'an Jiaotong University, China

    5 publications · 382 citations

    5

    h-index

1–5 of 633

Venues

  1. 31

    h-index

  2. 12

    h-index

  3. IEEE Transactions on Signal Processing

    11 publications · 889 citations

    11

    h-index

  4. International Conference on Information Fusion

    Conference · 6 publications · 216 citations

    6

    h-index

  5. Automatica

    6 publications · 357 citations

    6

    h-index

1–5