Evidence map›Paper›PMID 33799343›Full record

ArticleSensors (Basel, Switzerland)2021

Error Analysis of Accelerometer- and Magnetometer-Based Stationary Alignment.

Felipe O Silva, Lucas P S Paiva, Gustavo S Carvalho

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Felipe O SilvaDepartment of Automatics, Federal University of Lavras, Lavras 37200-900, Brazil.ORCID 0000-0002-3715-7023
Lucas P S PaivaDepartment of Automatics, Federal University of Lavras, Lavras 37200-900, Brazil.
Gustavo S CarvalhoDepartment of Automatics, Federal University of Lavras, Lavras 37200-900, Brazil.

Funding

Conselho Nacional de Desenvolvimento Científico e Tecnológico 313160/2019-8Coordenação de Aperfeiçoamento de Pessoal de Nível Superior 88881.169927/2018-01Empresa Brasileira de Pesquisa Agropecuária 212-20/2018Fundação de Amparo à Pesquisa do Estado de Minas Gerais CAG-APQ-01449-17
6 · The paper itself

Abstract

This paper revisits the stationary attitude initialization problem, i.e., the stationary alignment, of Attitude and Heading Reference Systems (AHRSs). A detailed and comprehensive error analysis is proposed for four of the most representative accelerometer- and magnetometer-based stationary attitude determination methods, namely, the Three-Axis Attitude Determination (TRIAD), the QUaternion ESTimator (QUEST), the Factored Quaternion Algorithm (FQA), and the Arc-TANgent (ATAN). For the purpose of the error analysis, constant biases in the accelerometer and magnetometer measurements are considered (encompassing, hence, the effect of hard-iron magnetism), in addition to systematic errors in the local gravity and Earth magnetic field models (flux density magnitude, declination angle, and inclination angle). The contributions of this paper are novel closed-form formulae for the residual errors (normality, orthogonality, and alignment errors) developed in the computed Direction Cosine Matrices (DCM). As a consequence, analytical insight is provided into the problem, allowing us to properly compare the performance of the investigated alignment formulations (in terms of ultimate accuracy), as well as to remove some misleading conclusions reported in previous works. The adequacy of the proposed error analysis is validated through simulation and experimental results.

Indexed as

AlgorithmsGravitationAccelerometryComputer SimulationHumansMagnetometryaccelerometerAHRSerror analysismagnetometernavigationstationary alignment

Identifiers

PMID33799343
PMCPMC8001527

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.