Year: 2022
Type: preprint
Abstract: For model-based estimation methods, the modeling that is as close to reality as possible makes a vital estimation result. In simple applications, it is sufficient to model a system with a single state space model. However, there are applications in which a system changes its behavior deterministically or stochastically. A previously defined model then describes the behavior of the system only inaccurately or is even no longer valid. The state of the art is to use more than one system models in parallel and to derive a suitable system estimate from them. In the literature, this is generally referred to as the Multiple Model (MM) method. Depending on the application and requirements, different methods exist for this purpose, which determine a single state estimate from a set of models. A frequently used representative of these methods is the Interacting Multiple Model (IMM) method which will be presented in this paper.
Source: arXiv (Cornell University)
Author: Dingler, Sebastian
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4285077565