An extensive analysis for the use of back propagation neural networks to perform the calibration of MEMS gyro bias thermal drift

Fontanella, Rita; Accardo, Domenico; Caricati, Egidio; Cimmino, Stefano; Simone, Domenico

doi:10.1109/plans.2016.7479760

Cited by 24 publications

(8 citation statements)

References 15 publications

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“…The network output is then compared with the desired output using a loss function that calculates the error values for every neuron in the network, starting at the output layer and propagating backwards through the entire network. This phase updates all the weights using a selected optimization function (15).…”

Section: Methodsmentioning

confidence: 99%

Deep Iris: Deep Learning for Gender Classification Through Iris Patterns

Khalifa¹,

Taha²,

Hassanien³

et al. 2019

Acta Inform Med

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Introduction: One attractive research area in the computer science field is soft biometrics. Aim: To Identify a person’s gender from an iris image when such identification is related to security surveillance systems and forensics applications. Methods: In this paper, a robust iris gender-identification method based on a deep convolutional neural network is introduced. The proposed architecture segments the iris from a background image using the graph-cut segmentation technique. The proposed model contains 16 subsequent layers; three are convolutional layers for feature extraction with different convolution window sizes, followed by three fully connected layers for classification. Results: The original dataset consists of 3,000 images, 1,500 images for men and 1,500 images for women. The augmentation techniques adopted in this research overcome the overfitting problem and make the proposed architecture more robust and immune from simply memorizing the training data. In addition, the augmentation process not only increased the number of dataset images to 9,000 images for the training phase, 3,000 images for the testing phase and 3,000 images for the verification phase but also led to a significant improvement in testing accuracy, where the proposed architecture achieved 98.88%. A comparison is presented in which the testing accuracy of the proposed approach was compared with the testing accuracy of other related works using the same dataset. Conclusion: The proposed architecture outperformed the other related works in terms of testing accuracy.

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Section: Methodsmentioning

confidence: 99%

Deep Iris: Deep Learning for Gender Classification Through Iris Patterns

Khalifa¹,

Taha²,

Hassanien³

et al. 2019

Acta Inform Med

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“…The numerical model of the heat sink is simplified as shown in 𝑣 = (𝑢 𝑖𝑛 , 0,0) 𝑇 𝑓 = 𝑇 𝑖𝑛 (12) where 𝑇 𝑖𝑛 = 25 ℃.…”

Section: Implementation Of Parameterized Fe Modelmentioning

confidence: 99%

“…Dzhashitov et al [11] carried out a research on nanostructure composite materials based on carbon nanotubes with high thermal conductivity, which could be applied in inertial devices to reduce the temperature variance. Fontanella et al [12] presented an innovative calibration method based on the use of Back-Propagation Neural Networks (BPNNs). The purpose of this work was to calibrate the surrogate model, which indicated the correlation between Micro-Electro-Mechanical System (MEMS) gyroscope null-voltage and temperature.…”

Section: Introductionmentioning

confidence: 99%

A surrogate assisted thermal optimization framework for design of pin-fin heat sink for the platform inertial navigation system

Luo

Wang

2020

Engineering Optimization

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In this study, in order to reduce the local high temperature of the platform in inertial navigation system (PINS), a pin-fin heat sink with staggered arrangement is designed. To reduce the dimension of the inputs and improve the efficiency of optimization, a feasible global sensitivity analysis (GSA) based on Kriging-High Dimensional Model Representation with DIviding RECTangles sampling strategy (DIRECT-KG-HDMR) is proposed. Compared with other GSA methods, the proposed method can indicate the effects of the structural and the material parameters on the maximum temperature at the bottom of the heat sink by using both sensitivity and coupling coefficients. From the results of GSA, it can be found that the structural parameters have greater effects on thermal performance than the material ones. Moreover, the coupling intensities between the structural and material parameters are weak. Therefore, the structural parameters are selected to optimize the thermal performance of the heat sink, and several popular optimization algorithms such as GA, DE, TLBO, PSO and EGO are used for the optimization. Moreover, steady thermal response of the PINS with the optimized heat sink is also studied, and its result shows that the maximum temperature of high temperature region of the platform is reduced by 1.09 ℃ compared with the PINS without the heat sink.

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“…This trend is highly non-linear and it can have a local abrupt change within a small temperature range [ 26 ]. Back-Propagation Neural Networks guarantee better performance on mapping than the traditional fitting method based on the application of polynomial fitting [ 28 , 29 ]. In fact, polynomials are not efficient to model these local changes of trend since they have fixed shapes as a function of their order.…”

Section: Standard Zupt Filtermentioning

confidence: 99%

An Innovative Strategy for Accurate Thermal Compensation of Gyro Bias in Inertial Units by Exploiting a Novel Augmented Kalman Filter

Fontanella

Accardo

Moriello

et al. 2018

Sensors

Self Cite

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This paper presents an innovative model for integrating thermal compensation of gyro bias error into an augmented state Kalman filter. The developed model is applied in the Zero Velocity Update filter for inertial units manufactured by exploiting Micro Electro-Mechanical System (MEMS) gyros. It is used to remove residual bias at startup. It is a more effective alternative to traditional approach that is realized by cascading bias thermal correction by calibration and traditional Kalman filtering for bias tracking. This function is very useful when adopted gyros are manufactured using MEMS technology. These systems have significant limitations in terms of sensitivity to environmental conditions. They are characterized by a strong correlation of the systematic error with temperature variations. The traditional process is divided into two separated algorithms, i.e., calibration and filtering, and this aspect reduces system accuracy, reliability, and maintainability. This paper proposes an innovative Zero Velocity Update filter that just requires raw uncalibrated gyro data as input. It unifies in a single algorithm the two steps from the traditional approach. Therefore, it saves time and economic resources, simplifying the management of thermal correction process. In the paper, traditional and innovative Zero Velocity Update filters are described in detail, as well as the experimental data set used to test both methods. The performance of the two filters is compared both in nominal conditions and in the typical case of a residual initial alignment bias. In this last condition, the innovative solution shows significant improvements with respect to the traditional approach. This is the typical case of an aircraft or a car in parking conditions under solar input.

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An extensive analysis for the use of back propagation neural networks to perform the calibration of MEMS gyro bias thermal drift

Cited by 24 publications

References 15 publications

Deep Iris: Deep Learning for Gender Classification Through Iris Patterns

Deep Iris: Deep Learning for Gender Classification Through Iris Patterns

A surrogate assisted thermal optimization framework for design of pin-fin heat sink for the platform inertial navigation system

An Innovative Strategy for Accurate Thermal Compensation of Gyro Bias in Inertial Units by Exploiting a Novel Augmented Kalman Filter

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