SMDS-based Rigid Body Localization

Fuente: arXiv
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Autores principales: Führling, Niclas, Abreu, Giuseppe, G., David González, Gonsa, Osvaldo
Formato: Preprint
Publicado: 2025
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author Führling, Niclas
Abreu, Giuseppe
G., David González
Gonsa, Osvaldo
author_facet Führling, Niclas
Abreu, Giuseppe
G., David González
Gonsa, Osvaldo
contents We consider a novel rigid body localization (RBL) method, based only on a set of measurements of the distances, as well as the angles between sensors of the vehicle to the anchor landmark points. A key point of the proposed method is to use a variation of the super multidimensional scaling (SMDS) algorithm, where only a minor part of the complex edge kernel is used, based on the available information, which in the case of RBL is anchor-to-anchor and target-to-target information. Simulation results illustrate the good performance of the proposed technique in terms of mean square error (MSE) of the estimates, compared also to the corresponding Cramér-Rao Lower Bound (CRLB).
format Preprint
id arxiv_https___arxiv_org_abs_2509_01223
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle SMDS-based Rigid Body Localization
Führling, Niclas
Abreu, Giuseppe
G., David González
Gonsa, Osvaldo
Signal Processing
We consider a novel rigid body localization (RBL) method, based only on a set of measurements of the distances, as well as the angles between sensors of the vehicle to the anchor landmark points. A key point of the proposed method is to use a variation of the super multidimensional scaling (SMDS) algorithm, where only a minor part of the complex edge kernel is used, based on the available information, which in the case of RBL is anchor-to-anchor and target-to-target information. Simulation results illustrate the good performance of the proposed technique in terms of mean square error (MSE) of the estimates, compared also to the corresponding Cramér-Rao Lower Bound (CRLB).
title SMDS-based Rigid Body Localization
topic Signal Processing
url https://arxiv.org/abs/2509.01223