Improved noise reduction for nonlinear PMDC motor dead zone and friction model using variants of extended Kalman filter with practical validation. [PDF]
Haider S +4 more
europepmc +1 more source
Trainable Quaternion Extended Kalman Filter with Multi-Head Attention for Dead Reckoning in Autonomous Ground Vehicles. [PDF]
Milam G +6 more
europepmc +1 more source
Detecting Critical Change in Dynamics Through Outlier Detection with Time‐Varying Parameters
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 +2 more
wiley +1 more source
Adaptive Distributed Student's T Extended Kalman Filter Employing Allan Variance for UWB Localization. [PDF]
Gao Y +6 more
europepmc +1 more source
An Extended Kalman Filter for Magnetic Field SLAM Using Gaussian Process Regression. [PDF]
Viset F, Helmons R, Kok M.
europepmc +1 more source
Burst Detection in Water Distribution System Using the Extended Kalman Filter
Donghwi Jung, Kevin Lansey
openalex +1 more source
Abstract Aims Mortality of patients with type 2 diabetes (T2D) after acute myocardial infarction (AMI) is tremendous and massively increased compared to non‐T2D individuals. The reasons are unclear. High‐density lipoprotein (HDL) conducts lipoprotection during AMI, leading to improved outcomes.
Jens Vogt +12 more
wiley +1 more source
Hybrid extended Kalman filter with Newton Raphson method for lifetime prediction of lithium-ion batteries. [PDF]
Fahmy HM, Hasanien HM, Alharbi M, Ji H.
europepmc +1 more source
Consistent Extended Kalman Filter-Based Cooperative Localization of Multiple Autonomous Underwater Vehicles. [PDF]
Zhang F, Wu X, Ma P.
europepmc +1 more source
Adaptive expectations and reaction to information
Abstract This paper develops a model combining adaptive expectations with noisy signals, and derives three coefficients and one impulse response function (IRF): the Coibion–Gorodnichenko (CG) coefficient capturing consensus under‐reaction to information, the Bordalo–Gennaioli–Ma–Shleifer coefficient capturing individual over‐reaction, the Kohlhas ...
Junyi Liao
wiley +1 more source

