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

Hanlon, B, Garcia-Fernandez, AF ORCID: 0000-0002-6471-8455 and Peng, B ORCID: 0000-0003-0152-3180 (2024) A Comparison Between Kalman-MLE and KalmanNet for State Estimation with Unknown Noise Parameters In: 2024 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI), 2024-9-4 - 2024-9-6.

Text A comparison between Kalman-MLE and KalmanNet.pdf - Author Accepted Manuscript Available under License Creative Commons Attribution. Download (437kB) | Preview

Abstract

This paper presents a comparison between the recently proposed KalmanNet for dynamic state estimation with unknown measurement and dynamic noise covariance matrices, and a classic approach to solve this problem. Given known transition and measurement functions and a training data set that consists of sequences of ground truth states and the associated measurements, KalmanNet learns the parameters of a network that aims to compute the Kalman gain. The classic approach we consider is to estimate the noise covariance matrices via maximum likelihood estimation (MLE) during training. Then, a Kalman filter with the estimated covariance matrices is used during testing (Kalman-MLE). The benefits of Kalman-MLE versus KalmanNet are shown via experiments in two linear-Gaussian systems, and a non-linear system.

Item Type:	Conference Item (Unspecified)
Uncontrolled Keywords:	4007 Control Engineering, Mechatronics and Robotics, 40 Engineering, 4001 Aerospace Engineering
Depositing User:	Symplectic Admin
Date Deposited:	13 Sep 2024 13:22
Last Modified:	23 May 2026 09:17
DOI:	10.1109/MFI62651.2024.10705758
Related Websites:	Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3184477
Disclaimer:	The University of Liverpool is not responsible for content contained on other websites from links within repository metadata. Please contact us if you notice anything that appears incorrect or inappropriate.