Evidence map›Paper›PMID 32050729›Full record

ArticleSensors (Basel, Switzerland)2020

In-Field Calibration of Triaxial Accelerometer Basedon Beetle Swarm Antenna Search Algorithm.

Pengfei Wang, Yanbin Gao, Menghao Wu, Fan Zhang, Guangchun Li

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

5 authors.

Pengfei WangCollage of Automation, Harbin Engineering University, Harbin 150001, Ch.
Yanbin GaoCollage of Automation, Harbin Engineering University, Harbin 150001, Ch.
Menghao WuCollage of Automation, Harbin Engineering University, Harbin 150001, Ch.
Fan ZhangCollage of Automation, Harbin Engineering University, Harbin 150001, Ch.
Guangchun LiCollage of Automation, Harbin Engineering University, Harbin 150001, Ch.

Funding

Chongqing Municipal Education Commission KJZD-K201804701National Natural Science Foundation of China 61803118
6 · The paper itself

Abstract

Traditional calibration method is usually performed with expensive equipments suchas three-axis turntable in a laboratory environment. However in practice, in order to ensure theaccuracy and stability of the inertial navigation system (INS), it is usually necessary to recalibratethe inertial measurement unit (IMU) without external equipment in the field. In this paper, anew in-field recalibration method for triaxial accelerometer based on beetle swarm antenna search(BSAS) algorithm is proposed. Firstly, as a new intelligent optimization algorithm, BSAS algorithmand its improvements based on basic beetle antennae search (BAS) algorithm are introduced indetail. Secondly, the nonlinear mathematical model of triaxial accelerometer is established forhigher calibration accuracy, and then 24 optimal measurement positions are designed by theoreticalanalysis. In addition, the calibration procedures are improved according to the characteristics of BSASalgorithm, then 15 calibration parameters in the nonlinear method are optimized by BSAS algorithm.Besides, the results of BSAS algorithm and basic BAS algorithm are compared by simulation, whichshows the priority of BSAS algorithm in calibration field. Finally, two experiments demonstrate thatthe proposed method can achieve high precision in-field calibration without any external equipment,and meet the accuracy requirements of the INS.

Indexed as

beetle swarm antenna search algorithmcalibrationinertial measurement unitintelligent optimization algorithmtriaxial accelerometer

Identifiers

PMID32050729
PMCPMC7039227

What OpenQuestion holds

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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.