The Unscented Kalman Filter

By Eric A. Wan, Rudolph van der Merwe. 2002

560 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/9af7d42b2112ff88dd8902283ab3d1b8f231eaf7

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Aerospace Engineering

1. John L. Crassidis
2. Yang Cheng
3. Markley
4. Juha Karhunen
5. F. Landis
6. Harri Valpola
7. Angelo Maria Sabatini
8. Jong-Hwan Kim

In Artificial Intelligence

1. John L. Crassidis
2. Yang Cheng
3. Juha Karhunen
4. Harri Valpola
5. Markley
6. F. Landis
7. Yoshua Bengio
8. Tapani Raiko

Citations in the corpus

• Survey of nonlinear attitude estimation methods (2007, methodology): “Methods to handle process noise for the computation of the predicted covariance and to handle nonlinearly appearing measurement noise are discussed in [29].”
• A comparison of unscented and extended Kalman filtering for estimating quaternion motion (2003, unknown): “= 2, + Kk(~k -Hk%L),”
• An Unsupervised Ensemble Learning Method for Nonlinear Dynamic State-Space Models (2002, methodology): “In Wan and Nelson (2001) and Wan and Merwe (2001), the unknown state and dynamics are estimated using extended Kalman filtering, while the observation mapping f is assumed to be the identity mapping.”