BIMSA > Seminar on Control Theory and Nonlinear Filtering Iterative Cubature Kalman filtering with General Robust Loss Function
Iterative Cubature Kalman filtering with General Robust Loss Function
Organizer
Shing Toung Yau
Speaker
Yangtianze Tao
Time
Friday, January 5, 2024 9:00 PM - 9:30 PM
Venue
Online
Abstract
An iterative Cubature Kalman filter based on a general robust loss function (GRICKF) is developed to solve the problem of non-Gaussian noise and nonlinear problem employed in state estimation. The algorithm employs a general robust loss function to address the non-Gaussian noise problem encountered during state estimation. The general robust loss function possesses the ability to convert into diverse M-estimation cost functions by utilizing various parameter values. As a result, GRICKF offers improved scalability and is capable of performing adeptly in more challenging noise environments. And the accuracy of the algorithm in nonlinear state estimation problems is enhanced through employing nonlinear augmented model ensemble prediction error and measurement error. The experimental results confirm that the algorithm has stronger robustness.