Work

A Comparison Between Kalman-MLE and KalmanNet for State Estimation with Unknown Noise Parameters

Year: 2024

Type: conference-paper

Source: IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)

DOI: 10.1109/mfi62651.2024.10705758

Authors: Bettina Hanlon, Ángel F. García-Fernández, Bei Peng

Institution: University of Liverpool

Language: English

Topic: Target Tracking and Data Fusion in Sensor Networks

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: closed

Funder: Engineering and Physical Sciences Research Council

Award: EPSRC Centre for Doctoral Training in Distributed Algorithms: the what, how and where of next-generation data science

OpenAlex ID: W4403278284

FWCI: 0.294

Cites: 19

Cited by: 1

Related to: 10