Noise Covariances Identification by MDM: Weighting, Recursion, and Implementation

Fuente: arXiv
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Main Authors: Kost, Oliver, Dunik, Jindrch, Straka, Ondrej
Format: Preprint
Published: 2024
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author Kost, Oliver
Dunik, Jindrch
Straka, Ondrej
author_facet Kost, Oliver
Dunik, Jindrch
Straka, Ondrej
contents The problem of noise covariance matrix identification of stochastic linear time-varying state-space models is addressed. The measurement difference method (MDM) is generalized to time-varying dimensions of the measurement and control. Three MDM identification techniques that differ in weighting used in the underlying least squares method are proposed. The techniques differ in estimate quality and computational complexity. In addition, recursive forms are designed for two techniques. The performance of the proposed techniques is analyzed using two numerical examples. The implementation of techniques is enclosed with the paper.
format Preprint
id arxiv_https___arxiv_org_abs_2412_06373
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Noise Covariances Identification by MDM: Weighting, Recursion, and Implementation
Kost, Oliver
Dunik, Jindrch
Straka, Ondrej
Signal Processing
The problem of noise covariance matrix identification of stochastic linear time-varying state-space models is addressed. The measurement difference method (MDM) is generalized to time-varying dimensions of the measurement and control. Three MDM identification techniques that differ in weighting used in the underlying least squares method are proposed. The techniques differ in estimate quality and computational complexity. In addition, recursive forms are designed for two techniques. The performance of the proposed techniques is analyzed using two numerical examples. The implementation of techniques is enclosed with the paper.
title Noise Covariances Identification by MDM: Weighting, Recursion, and Implementation
topic Signal Processing
url https://arxiv.org/abs/2412.06373