Noise reduction in optical gyroscope signals based on hybrid approaches

AbdelZaher, Hesham M.; El-Dokany, Ibrahim M.; El-Dolil, Sami A.; Oraby, Osama A.; Dessouky, Moawad I.; El‐Fishawy, Adel S.; El‐Rabaie, El‐Sayed M.; Abd-El-Samie, Fathi. E.

doi:10.1007/s12596-020-00617-3

Cited by 4 publications

(3 citation statements)

References 18 publications

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“…Microelectromechanical systems (MEMS) have become the basic components of civil navigation because of their low-cost performance, small size and weight, and strong adaptability [1]. Among these ways for improving navigation accuracy, the most effective one is to improve accuracy of MEMS gyroscopes [2]. Improving navigation accuracy through gyroscope noise reduction is a must for civil navigation.…”

Section: Introductionmentioning

confidence: 99%

A hybrid denoising method for gyroscopes based on multiple screening

Rong,

Jin,

Wang

et al. 2023

Metrology and Measurement Systems

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To reduce the random error of microelectromechanical system (MEMS) gyroscope, a hybrid method combining improved empirical mode decomposition (EMD) and least squares algorithm (LS) is proposed. Firstly, based on the multiple screening mechanism, intrinsic mode functions (IMFs) from the first decomposition are divided into noise IMFs, strong noise mixed IMFs, weak noise mixed IMFs and signal IMFs. Secondly, according to their characteristics, they are processed again. IMFs from the second decomposition are divided into noise IMFs and signal IMFs. Finally, useful signal is gathered to obtain the final denoising signal. Compared with some other denoising methods proposed in recent years, the experimental results show that the proposed method has obvious advantages in suppressing random error, greatly improving the signal quality and improving the accuracy of inertial navigation.

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Section: Introductionmentioning

confidence: 99%

A hybrid denoising method for gyroscopes based on multiple screening

Rong,

Jin,

Wang

et al. 2023

Metrology and Measurement Systems

View full text Add to dashboard Cite

show abstract

“…There are various types of gyroscopes: mechanical, micro electrical mechanical sensors (MEMS) and optical (Abdelzaher et al, 2021). The mechanical gyroscope is made up of a free wheel that occupy a 3-dimensional space.…”

Section: Gyroscopementioning

confidence: 99%

“…The transformation of angular motion into an electrical signal is carried out using Coriolis effect and is based on the movement of a metal sphere connected to a group of springs. This has led to the development of the micro-electro-mechanical system (MEMS) (Bergh et al, 1984;Abdelzaher et al, 2021). The MEMS gyroscope is made up of a mass that is attached to a solid frame by springs.…”

Section: Gyroscopementioning

confidence: 99%

Computerised accelerometric machine learning techniques and statistical developments for human balance analysis

Ojie¹

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Balance maintenance is crucial to participating in the activities of daily life. Balance is often considered as the ability to maintain the centre of mass (COM) position within the base of support. Primarily, to maintain balance, reliance is placed on the balance related sensory systems i.e., the visual, proprioceptive and vestibular. Several factors can affect a person’s balance such as neurological diseases, ageing, medication and obesity etc. To gain insight into the balance operations, studies rely on statistical and machine learning techniques. Statistical techniques are used for inferencing while machine learning techniques proved effective for interpretation. The focus of this study was on the issues encountered in human balance analysis such as the quantification of balance by relevant features, the relationships between COM and ground projected body sway, the performance of various sensory systems in balance analysis, and their relationships between the directions of body sway (i.e., mediolateral (ML) and anteriorposterior (AP)). A portable wireless accelerometry device was developed, balance analysis methods based on the inverted pendulum were devised and evaluated for their accuracy and reliability against a setup designed to allow manual balance measurements. Balance data were collected from 23 healthy adult subjects with the mean (standard deviation) of the age, height and weight: 24.5 (4.0) years, 173.6 (6.8) cm, and 72.7 (9.9) kg respectively. The accelerometry device was attached to the subjects at the approximate position of the illac crest, while they performed 30 seconds trials of the four conditions associated with a standard balance test called the modified Clinical Test of Sensory Interaction and Balance (mCTSIB). These required standing on a hard (ground) surface with the eyes open, standing on hard surface with the eyes closed, standing on a compliant surface (sponge, 10 cm thick) with the eyes open and standing on a compliant surface with the eyes closed. Statistical and machine learning techniques such as t-test, Wilcoxon signed-rank test, the Mann-Whitney U test, Analysis of variance (ANOVA), Kruskal-Wallis test, Friedman test, correlation analysis, linear regression, Bland and Altman analysis, principal component analysis (PCA), K-means clustering, and Kohonen neural network (KNN) were employed for interpreting the measurements. The findings showed close agreement between the developed balance analysis methods and the related measurements from the manual setup for balance analysis. The COM was observed to be responsible for differing amount of sway across the subjects and could affect both the angle and ground projected sway. The AP direction was more sensitive to sway than the ML direction. The subjects were observed to depend more on their proprioceptive system to control balance. The proprioceptive system was observed to have a greater impact in controlling the AP velocity of the subjects as compared to their visual system. The proprioceptive system had no impact on the ML velocity. The visual system was responsible for the control of the ML velocity and for reducing the acceleration in both directions. It was concluded that for comparison of postural sway information, subjects with closely related COM positions should be compared, comparison should be carried out in respect to the base of their support. The sway normalisation by dividing with COM position should be performed to reduce the obscuring effect of the COM. Enhancement of the proprioceptive system should be carried out to reduce the AP velocity while enhancement of the visual system should be used to reduce the ML sway and acceleration in ML and AP directions. The velocity in the AP direction should be used to examine the performance of the proprioceptive system while the ML velocity and acceleration should be used for the visual system. The vestibular system characterised sway more in the AP direction, and hence, the AP direction should be used to examine its performance in balance.

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Radio over fiber system-based direct modulation VCSEL optical source

Jalal

Al-Maqdici

2023

J Opt

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Noise reduction in optical gyroscope signals based on hybrid approaches

Cited by 4 publications

References 18 publications

A hybrid denoising method for gyroscopes based on multiple screening

A hybrid denoising method for gyroscopes based on multiple screening

Computerised accelerometric machine learning techniques and statistical developments for human balance analysis

Radio over fiber system-based direct modulation VCSEL optical source

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