Temperature Energy Influence Compensation for MEMS Vibration Gyroscope Based on RBF NN‐GA‐KF Method

Cao, Huiliang; Zhang, Yingjie; Shen, Chong; Liu, Yu; Wang, Xinwang

doi:10.1155/2018/2830686

Cited by 64 publications

(52 citation statements)

References 27 publications

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“…Step 7: by selecting appropriate N and K, the calculated optical flow direction in the N layer can be obtained from Equation (5), and the initial optical flow estimation vector of the next layer can be obtained by substituting it into Equation (6). At last, the iterative calculation of Equation (7) is carried out once to obtain the optical flow estimation vector of the original image sequence and obtain the optical flow value result [39,40].…”

Section: Pyramid Lk Algorithm Based On Mask-r-cnn and K-meansmentioning

confidence: 99%

“…However, the premise of the UAV consummate flight during the execution of the mission is precision navigation. Precise aircraft navigation information such as velocity is the basis of the stability control of the UAV [1][2][3][4][5]. The most commonly techniques used to obtain vehicle velocity include the inertial navigation system (INS) [6][7][8], global position system (GPS) [9,10], geomagnetic navigation and a GPS/INS [11][12][13] integrated navigation system.…”

Section: Introductionmentioning

confidence: 99%

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An Improved Optical Flow Algorithm Based on Mask-R-CNN and K-Means for Velocity Calculation

et al. 2019

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Aiming at enhancing the accuracy and reliability of velocity calculation in vision navigation, an improved method is proposed in this paper. The method integrates Mask-R-CNN (Mask Region-based Convolutional Neural Network) and K-Means with the pyramid Lucas Kanade algorithm in order to reduce the harmful effect of moving objects on velocity calculation. Firstly, Mask-R-CNN is used to recognize the objects which have motions relative to the ground and covers them with masks to enhance the similarity between pixels and to reduce the impacts of the noisy moving pixels. Then, the pyramid Lucas Kanade algorithm is used to calculate the optical flow value. Finally, the value is clustered by the K-Means algorithm to abandon the outliers, and vehicle velocity is calculated by the processed optical flow. The prominent advantages of the proposed algorithm are (i) decreasing the bad impacts to velocity calculation, due to the objects which have relative motions; (ii) obtaining the correct optical flow sets and velocity calculation outputs with less fluctuation; and (iii) the applicability enhancement of the optical flow algorithm in complex navigation environment. The proposed algorithm is tested by actual experiments. Results with superior precision and reliability show the feasibility and effectiveness of the proposed method for vehicle velocity calculation in vision navigation system.

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Section: Pyramid Lk Algorithm Based On Mask-r-cnn and K-meansmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Improved Optical Flow Algorithm Based on Mask-R-CNN and K-Means for Velocity Calculation

et al. 2019

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“…Micro-Electro-Mechanical System Inertial navigation system (MEMS-INS) demonstrates its unique superiority, which can calculate the next-point location based on the continuously measured self-motion velocity and direction information, rather than external information, so it has a vast application scope. Whereas, the navigation information of INS is generated by the integration of velocity and direction information measured by sensors, so the error will increase over time, thereby resulting in poor positioning accuracy over a long time [1][2][3]. Additionally, inertial sensors suffer from large measurement uncertainty at slow motion [4].…”

Section: Introductionmentioning

confidence: 99%

Brain-Like Navigation Scheme based on MEMS-INS and Place Recognition

et al. 2019

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Animals have certain cognitive competence about the environment so they can correct their navigation errors. Inspired by the excellent navigational behavior of animals, this paper proposes a brain-like navigation scheme to improve the accuracy and intelligence of Micro-Electro-Mechanical System based Inertial Navigation Systems (MEMS-INS). The proposed scheme employs vision to acquire external perception information as an absolute reference to correct the position errors of INS, which is established by analyzing the navigation and error correction mechanism of rat brains. In addition, to improve the place matching speed and precision of the system for visual scene recognition, this paper presents a novel place recognition algorithm that combines image scanline intensity (SI) and grid-based motion statistics (GMS) together which is named the SI-GMS algorithm. The proposed SI-GMS algorithm can effectively reduce the influence of uncertain environment factors on the recognition results, such as pedestrians and vehicles. It solves the problem that the matching result will occasionally go wrong when simply using the scanline intensity (SI) algorithm, or the slow matching speed when simply using grid-based motion statistics (GMS) algorithm. Finally, an outdoor Unmanned Aerial Vehicle (UAV) flight test is carried out. Based on the reference information from the high-precision GPS device, the results illustrate the effectiveness of the scheme in error correction of INS and the algorithm in place recognition.

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“…RBF NN based on GA has been proposed and successfully applied to the temperature drift model of the MEMS gyroscope [25]. Unlike previous accelerometer temperature drift models, a new fusion algorithm (RBF + NN + GA + KF combined with temperature drift model) was proposed in this paper to make the accelerometer more accurate.…”

Section: Introductionmentioning

confidence: 99%

Temperature Drift Compensation for High-G MEMS Accelerometer Based on RBF NN Improved Method

Zhu

Pang²,

Zhang

et al. 2019

Applied Sciences

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In this paper, the method for compensating the temperature drift of high-G MEMS accelerometer (HGMA) is proposed, including radial basis function neural network (RBF NN), RBF NN based on genetic algorithm (GA), RBF NN based on GA with Kalman filter (KF), and the RBF NN + GA + KF method compensated by the temperature drift model. First, this paper introduces an HGMA structure working principle, conducts a finite element analysis, and produces the results. The simulation results show that the HGMA working mode is the 1st order mode, and its resonant frequency is 408 kHz. The 2nd order mode resonant frequency is 667 kHz, and the gap with the first mode is 260 kHz, indicating that the coupling movement between the two modes is tiny, so the HGMA has good linearity. Then, a temperature experiment is performed to obtain the output value of HGMA. The output values of HGMA are analyzed and optimized by using the algorithms proposed in this paper. The processing results show that the RBF NN + GA + KF method compensated by the temperature drift model achieves the best denoing consequent. The processing results show that the temperature drift of the HGMA is effectively compensated. The final results show that acceleration random walking improved from 17130 g/h/Hz0.5 to 765.3 g/h/Hz0.5, and bias stability improved from 4720 g/h to 57.27 g/h, respectively. The results show that after using the RBF NN + GA + KF method, combined with the temperature drift model, the temperature drift trend and noise characteristics of HGMA are well optimized.

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Temperature Energy Influence Compensation for MEMS Vibration Gyroscope Based on RBF NN‐GA‐KF Method

Cited by 64 publications

References 27 publications

An Improved Optical Flow Algorithm Based on Mask-R-CNN and K-Means for Velocity Calculation

An Improved Optical Flow Algorithm Based on Mask-R-CNN and K-Means for Velocity Calculation

Brain-Like Navigation Scheme based on MEMS-INS and Place Recognition

Temperature Drift Compensation for High-G MEMS Accelerometer Based on RBF NN Improved Method

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