Sigma-point Filtering

Sigma-Point Filtering is a Bayesian filtering method that belongs to the class of numerical approximation techniques for nonlinear state estimation, distinguished by its use of a deterministic set of weighted sample points—sigma points—to approximate the mean and covariance of probability distributions through the unscented transformation, thereby enabling accurate propagation of uncertainty in nonlinear systems.

143

Publications

10.6K

Citations

314

Authors

146

Institutions

Publications per year

2017–2026

42

Authors

314

  1. SS
    Simo Särkkä

    Aalto University, Finland

    10 publications · 918 citations

    10

    h-index

  2. EA
    Eric A. Wan

    Oregon Health & Science University, United States

    7 publications · 1.1K citations

    7

    h-index

  3. ÁF
    Ángel F. García‐Fernández

    University of Liverpool, United Kingdom

    6 publications · 262 citations

    6

    h-index

  4. BC
    Bingbo Cui

    Jiangsu University, China

    5 publications · 254 citations

    5

    h-index

  5. XC
    Xiyuan Chen

    Southeast University, China

    4 publications · 221 citations

    4

    h-index

1–5 of 314

Venues

  1. IEEE Transactions on Signal Processing

    6 publications · 834 citations

    6

    h-index

  2. IEEE Transactions on Automatic Control

    4 publications · 634 citations

    4

    h-index

  3. AIAA Guidance, Navigation, and Control Conference and Exhibit

    Conference · 4 publications · 474 citations

    4

    h-index

  4. 4

    h-index

  5. Sensors

    4 publications · 55 citations

    4

    h-index

1–5