Results 191 to 200 of about 95,986 (277)

An adaptive extended Kalman filter observer-for permanent magnet synchronous motor position sensorless control systems. [PDF]

Sci Rep
Yingjun S, Zhenglong W, Yuanyuan F.
europepmc   +1 more source

Capability Demonstration of a JEDI‐Based System for TEMPO Assimilation: System Description and Evaluation

Journal of Advances in Modeling Earth Systems, Volume 18, Issue 5, May 2026.
Abstract The launch of the Tropospheric Emissions: Monitoring of Pollution (TEMPO) mission in 2023 marked a new era in air quality monitoring by providing high‐frequency, geostationary observations of column NO2 across most of North America. In this study, we present the first implementation of a TEMPO NO2 data assimilation system using the Joint ...
Maryam Abdi‐Oskouei, Jérôme Barré, Shih‐Wei Wei, Sarah Lu, Ashley Griffin, Clementine Hardy Gas, Francois Hebert, Stephen Herbener, Eric Lingerfelt, Evan Parker, Christian Sampson, Steve Vahl, Fabio Diniz, Ben Johnson, Cheng Dang, Yannick Tremolet, Benjamin Ruston, Viral Shah, Emma Knowland, Ricardo Todling, Ronald Gelaro, Caroline Nowlan, Gonzalo Gonzalez Abad, Xiong Liu, Brian McDonald, Kristen Zuraski, Jeff Peischl, Caroline Womack, Laura Judd, Thomas Hanisco, Benjamin Menetrier, Cory Martin, Daniel Holdaway, Anna Shlyaeva, Dom Heinzeller, Steven Pawson, Thomas Auligne   +36 more
wiley   +1 more source

Estimating Bus Mass Using a Hybrid Approach: Integrating Forgetting Factor Recursive Least Squares with the Extended Kalman Filter. [PDF]

Sensors (Basel)
Du J, Wang Q, Yuan X.
europepmc   +1 more source

Introducing Reflected GNSS TEC Data Into ANCHOR Ionospheric Data Assimilation Model

Space Weather, Volume 24, Issue 5, May 2026.
Abstract ANCHOR is a novel data assimilation (DA) algorithm developed at the U.S. Naval Research Laboratory to improve ionospheric nowcasting by increasing accuracy and decreasing computational cost. As a parameterized DA model, ANCHOR represents the ionosphere using physical parameters such as the F2 layer peak (Nm $Nm$F2), which characterizes a ...
Brenna Royersmith, Victoriya V. Forsythe, Brian Breitsch, Meghan R. Burleigh, Kate A. Zawdie, Y. Jade Morton   +5 more
wiley   +1 more source

Calibration Method for Relativistic Navigation System Using Parallel Q-Learning Extended Kalman Filter. [PDF]

Sensors (Basel)
Xiong K, Zhao Q, Yuan L.
europepmc   +1 more source

Comparing Assimilation of Remotely Sensed Discharge Derived From SWOT and Landsat Into a Global Hydrological Model

Water Resources Research, Volume 62, Issue 5, May 2026.
Abstract The Surface Water and Ocean Topography (SWOT) satellite has enabled river discharge estimation for global rivers including ungauged regions. This remotely sensed discharge (SwRSQ) generated from SWOT's cloud‐penetrating measurements of river widths, water surface elevation, and slope is expected to have improved accuracy and spatiotemporal ...
Y. Ishikawa, D. Yamazaki, C. J. Gleason, N. Hanasaki, S. Kotsuki, D. Shiojiri, E. Friedmann   +6 more
wiley   +1 more source

A Double Extended Kalman Filter Algorithm for Weakening Non-Line-of-Sight Errors in Complex Indoor Environments Based on Ultra-Wideband Technology. [PDF]

Sensors (Basel)
Xu S, Liu Q, Lin M, Wang Q, Chen K.
europepmc   +1 more source

Varying the Combination of Hydrological Models in Time and Space: Toward a More Accurate Representation of Streamflow in Large‐Sample Hydrology

Water Resources Research, Volume 62, Issue 5, May 2026.
Abstract Accurate predictions of streamflow are needed to manage water resources, evaluate flooding risks, and support agriculture and industry. Many hydrological studies rely on a single model structure and parameter set applied uniformly across space and held fixed over time, limiting their ability to represent spatiotemporal changes in hydrological ...
C. Thébault, W. J. M. Knoben, N. Addor, A. J. Newman, M. P. Clark   +4 more
wiley   +1 more source

State Estimation for Quadruped Robots on Non-Stationary Terrain via Invariant Extended Kalman Filter and Disturbance Observer. [PDF]

Sensors (Basel)
Wan M, Liu D, Wu J, Li L, Peng Z, Liu Z.
europepmc   +1 more source

Detecting Critical Change in Dynamics Through Outlier Detection with Time‐Varying Parameters

British Journal of Mathematical and Statistical Psychology, Volume 79, Issue 2, Page 237-262, May 2026.
Abstract Intensive longitudinal data are often found to be non‐stationary, namely, showing changes in statistical properties, such as means and variance‐covariance structures, over time. One way to accommodate non‐stationarity is to specify key parameters that show over‐time changes as time‐varying parameters (TVPs). However, the nature and dynamics of
Meng Chen, Michael D. Hunter, Sy‐Miin Chow   +2 more
wiley   +1 more source