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A novel fusion algorithm is proposed based on Improved Adaptive Wavelet Threshold De-noising (IAWTD), C-means Support Vector Machine (CSVM) and Ensemble Empirical Mode Decomposition (EEMD) method to eliminate the humidity drift of MEMS gyroscope. Firstly, the IAWTD method is employed to decrease the humidity drift component in MEMS gyroscope output signal. Then, the humidity drift compensation model is established: the input elements are the relative humidity, the change rate of relative humidity and the humidity drift, and the output is the compensated MEMS gyroscope output signal by EEMD method. In order to verify the compensation effect of the fusion algorithm, the gyroscope outputs are collected and analyzed with the relative humidity ranged from 40% to 90% based on the temperature varying from 20°C to 60°C. The results show that the IAWTD-CSVM-EEMD method significantly reduces the influence of relative humidity drift on the gyroscope output, according to the quantitative analysis of Allan variance, the quantization noise of the gyroscope output decreases by 87.78%, 96.37%, 97.77%, 99.17% and 92.62% respectively under the relative humidity ranging from 40% to 90%, as the temperature rose from 20 °C to 60 °C at intervals of 10 °C. In addition, the bias stability decreases by 96.9%, 99.41%, 99.1%, 99.46%, and 99.78% respectively and the angle random walk decreases by 88.16%, 96.54%, 98.16%, 94.43%, and 92.05% respectively at different temperatures. It is worth mentioning that, to further verify the applicability of the fusion algorithm, a group of comparative experiments are added to consider the influence of temperature changes on the gyroscope output under different relative humidity. The experimental results show that the quantization noise, bias stability and angle random walk of the MEMS gyroscope are significantly reduced compared with the original output after processing by IAWTD-CSVM-EEMD. Therefore, the method proposed in this paper is beneficial to reduce the humidity drift in the MEMS gyroscope output.

Because the dual-mass MEMS gyroscope’s output is greatly influenced by temperature, which can lead to errors that cannot be ignored. To solve this problem, a novel compensation method is proposed: a parallel processing algorithm, which integrates the Permutation entropy (PE), Local Characteristic-scale Decomposition (LCD) and Adaptive network-based fuzzy inference system (ANFIS). Firstly, LCD is used to decompose the output which contains temperature noises and drifts into a trend component and several intrinsic scale components (ISC), according to autocorrelation and complexity, three different categories will be obtained by PE: pure noise output, mixed output, and drift output. The different processes are as follows, the noise output is discarded, the mixed output is filtered by SG (Savitzky-Golay filter), then dual ANFIS is applied. Since the drift output completely reflects the temperature characteristics, the degree of non-linearity is high, the ANFIS with complex rules is used for processing. And the mixed output is composed of intermediate layer modes, containing a relatively small amount of temperature characteristics, simple rule ANFIS is adopted for processing. Finally, the signal is reconstructed. After that, the temperature error experiment is carried out, the result shows the method can effectively eliminate the error and compensate for the drift, it has a fast convergence speed and good effect, and has the advantage of good compensation efficiency.

MEMS-Inertialsensoren sind oszillatorische elektromechanische Bauelemente, die Relativbewegungen in einem Inertialsystem erfassen. Energieverluste in diesen Systemen, ausgedru��ckt als Gu��tefaktor Q, haben direkten Einfluss auf die absolute Messgenauigkeit der Sensoren, beispielsweise wenn sie fu��r Navigation bei Ausfall des GPS-Signals eingesetzt werden. Ein Mechanismus, der den Gu��tefaktor reduzieren kann, ist Energieverlust des oszillierenden Systems durch Gasda��mpfung. Indem der Sensor in einer evakuierten Kaverne betrieben wird, kann dieser Einfluss reduziert werden. Aufgrund der knappen Platzverha��ltnisse, die bei vielen Anwendungen fu��r die einzelnen Bauelemente zur Verfu��gung stehen, wird das Geha��use des Inertialsensors mo��glichst platzsparend ausgefu��hrt. Aufgrund der fortschreitenden Miniaturisierung vieler Bauelemente und somit auch der Kavernenvolumina von Inertialsensoren wird zuku��nftig der Einfluss geringfu��giger Vera��nderungen der Da��mpfungsatmospha��re in Sensorkavernen auf die Zuverla��ssigkeit der Sensoren immer gro����er. Die Versuche in dieser Arbeit zeigen, dass die Sensorkavernen trotz Vakuumwaferbondens Wasserstoff enthalten, der durch Desorbate, die im Verlauf der Sensorherstellung auftreten, nach abgeschlossener Prozessierung in den Kavernen eingeschlossen ist. Messungen anhand von Belastungstests zur beschleunigten Alterung von Sensorelementen in dieser Arbeit deuten zudem darauf hin, dass es Wasserstoffmoleku��len trotz Bemu��hungen um eine hermetische Abdichtung der Sensorkavernen gelingt, aus den Sensorkavernen hinaus zu diffundieren, was den Qualita��tsfaktor des Systems vera��ndert und die Lebensdauer des Sensors beeinflusst. In dieser Arbeit werden sowohl die Quellen des auffindbaren Wasserstoffs im Sensoraufbau, als auch Diffusionspfade zwischen Innen- und Au��enseite der Sensorkavernen identifiziert. Fu��r jeden der betrachteten Mechanismen, die den Gu��tefaktor beeintra��chtigen ko��nnen, werden Ma��nahmen vorgestellt, mit denen sich unerwu��nschte Vera��nderungen der Da��mpfungsatmospha��re wirksam und langfristig vermeiden lassen. Dazu geho��ren Ausheizprozesse vor Verschluss der Kavernen, Unterbrechen der Diffusionspfade durch Layoutanpassungen, sowie ein Ansatz fu��r die Verwendung von Materialien mit absorbierender Wirkung in den Kavernen, deren thermische Aktivierung mittels Laserstrahlung erfolgt.

This paper presents the characterization of the modal frequencies and the modal orientation of the axisymmetric resonators in Coriolis vibratory gyroscopes based on the approaches of the frequency sweep and the ring down. The modal frequencies and the orientation of the stiffness axis are the key parameters for the mechanical correction of the stiffness imperfections. The frequency sweep method utilizes the zero and the poles in the magnitude-frequency responses of the two-dimensional transfer function to extract the modal orientation information within the frequency domain. The ring down method makes use of the peak and the valley values of the beat signals at the readout electrodes to obtain the modal orientation and the coefficient of the nonlinear stiffness directly within the time domain. The proposed approaches were verified via a silicon ring resonator designed for gyroscopic sensing and the modal information from the experiments exhibited a good agreement between the methods of the frequency sweep and the ring down.

Video stabilization is essential for long-range electro-optical systems, especially in situations when the field of view is narrow, since the system shake may produce highly deteriorating effects. It is important that the stabilization works for different camera types, i.e., different parts of the electromagnetic spectrum independently of the weather conditions and any form of image distortion. In this paper, we propose a method for real-time video stabilization that uses only gyroscope measurements, analyze its performance, and implement and validate it on a real-world professional electro-optical system developed at Vlatacom Institute. Camera movements are modeled with 3D rotations obtained by integration of MEMS gyroscope measurements. The 3D orientation estimation quality depends on the gyroscope characteristics, we provide a detailed discussion on the criteria for gyroscope selection in terms of the sensitivity, measurement noise, and drift stability. Furthermore, we propose a method for improving the unwanted motion estimation quality using interpolation in the quaternion domain. We also propose practical solutions for eliminating disturbances originating from gyro bias instability and noise. In order to evaluate the quality of our solution, we compared the performance of our implementation with two feature-based digital stabilization methods. The general advantage of the proposed methods is its drastically lower computational complexity, hence, it can be implemented for a low price independent of the used electro-optical sensor system.

In high-reliability applications, the health condition of the MEMS gyroscope needs to be known in real time to ensure that the system does not fail due to the wrong output signal. Because the MEMS gyroscope self-test based on the principle of electrostatic force cannot be performed during the working state. We propose that by monitoring the quadrature error signal of the MEMS gyroscope in real time, an online self-test of the MEMS gyroscope can be realized. The correlation between the gyroscope’s quadrature error amplitude signal and the gyroscope scale factor and bias was theoretically analyzed. Based on the sixteen-sided cobweb-like MEMS gyroscope, the real-time built-in self-test (BIST) method of the MEMS gyroscope based on the quadrature error signal was verified. By artificially setting the control signal of the gyroscope to zero, we imitated several scenarios where the gyroscope malfunctioned. Moreover, a mechanical impact table was used to impact the gyroscope. After a 6000 g shock, the gyroscope scale factor, bias, and quadrature error amplitude changed by −1.02%, −5.76%, and −3.74%, respectively, compared to before the impact. The gyroscope failed after a 10,000 g impact, and the quadrature error amplitude changed −99.82% compared to before the impact. The experimental results show that, when the amplitude of the quadrature error signal seriously deviates from the original value, it can be determined that the gyroscope output signal is invalid.

In this paper, a minimum-learning-parameter (MLP) based neural control method is proposed for micro-electro-mechanical system (MEMS) gyroscope with prescribed performance and input quantization. For the first time, a logarithmic quantizer (LQ) is employed to generate smooth input control signal for MEMS gyroscope, which greatly reduces the communication data size as well as actuator bandwidth. To improve the performance of MEMS gyroscope in the presence of quantization error, a prescribed performance control scheme consisting of preselected performance boundaries and an error transformation is utilized, such that preselected transient and steady-state properties can be assured. In contrast to the neural control strategies subject to the issue of learning explosion, a MLP-based neural network (NN) is introduced to estimate the unknown uncertainties using the norm of neural weight. To eliminate the effect of quantization error induced by LQ, a robust quantized control is designed to further ensure the closed-loop system suffering from discontinuous dynamics with prescribed ultimately uniformly bounded (UUB) performance. In the end, a series of simulations are presented to validate the superiority of the proposed control methodology.

This paper investigates an event-triggered output feedback control problem with guaranteed tracking performance for microelectromechanical system (MEMS) gyroscope subject to the lumped disturbances, including parameter uncertainties, coupling between driving mode and sensing mode, and unknown external disturbances. Firstly, to make the system output tracking errors evolve within the predetermined performance envelopes, a prescribed performance control (PPC) is employed in our controller design, such that a prescribed trajectory tracking with preselected settling time and steady-state tracking accuracy can be ensured. Besides, to simultaneously realize disturbance rejection and estimation of the immeasurable velocity state, linear extended state observers (LESOs) are designed to reconstruct the lumped disturbances and immeasurable velocity states for both MEMS gyroscope driving and sensing modes. In general, excessively energy and resource consumption are fundamentally neglected in most of the existing output feedback controllers for MEMS gyroscope, which will dramatically reduce the operational time of the close-loop system. To address this problem, we embed a fixed threshold event-triggered mechanism into the controller design procedure, such that the communication data size can be drastically reduced without sacrificing the tracking accuracy. Finally, to obtain the close-loop stability for MEMS gyroscope, robust control laws are designed to compensate for the measurement error and estimation error, meanwhile Zeno phenomena can be effectively eliminated. The simulation results and comparisons validate the effectiveness of the proposed scheme.

To eliminate the noise and temperature drift in an Micro-Electro-Mechanical Systems (MEMS) gyroscope’s output signal for improving measurement accuracy, a parallel processing model based on Multi-objective particle swarm optimization based on variational modal decomposition-time-frequency peak filter (MOVMD–TFPF) and Beetle antennae search algorithm- Elman neural network (BAS–Elman NN) is established. Firstly, variational mode decomposition (VMD) is optimized by multi-objective particle swarm optimization (MOPSO); then, the best decomposition parameters [kbest,abest] can be obtained. Secondly, the gyroscope output signals are decomposed by VMD optimized by MOPSO (MOVMD); then, the intrinsic mode functions (IMFs) obtained after decomposition are classified into a noise segment, mixed segment, and drift segment by sample entropy (SE). According to the idea of a parallel model, the noise segment can be discarded directly, the mixed segment is denoised by time-frequency peak filtering (TFPF), and the drift segment is compensated at the same time. In the compensation part, the beetle antennae search algorithm (BAS) is adopted to optimize the network parameters of the Elman neural network (Elman NN). Subsequently, the double-input/single-output temperature compensation model based on the BAS-Elman NN is established to compensate the drift segment, and these processed segments are reconstructed to form the final gyroscope output signal. Experimental results demonstrate the superiority of this parallel processing model; the angle random walk of the compensated gyroscope output is decreased from 0.531076 to 5.22502 × 10−3°/h/√Hz, and its bias stability is decreased from 32.7364°/h to 0.140403°/h, respectively.

Conference Name: 2021 21st International Conference on Solid-State Sensors, Actuators and Microsystems (Transducers) Date of Conference: 20-24 June 2021 Structural imperfections affect the performance of MEMS vibrating ring gyroscopes (VRGs), dominantly in terms of quadrature error and frequency split. We consider a VRG with a ring subjected to width nonuniformity and supported by an imperfect suspension. This reflects the scenario of nonuniform etching, common in microfabrication processes. We show that the ring's width nonuniformity mainly results in a rotation of the mode-shapes with respect to those of a perfect ring while an imperfect suspension induces frequency split between the modes. On this basis, we calculate the quadrature error in the gyroscope's output. As an example, a 4° mode-shape rotation and 15Hz frequency split in a 60kHz VRG generate an approximate quadrature error of 1000°/s.

Micro-electro-mechanical system inertial measurement unit (MEMS-IMU), a core component in many navigation systems, directly determines the accuracy of inertial navigation system, however, MEMS-IMU system is often affected by various factors such as environmental noise, electronic noise, mechanical noise and manufacturing error. These can seriously affect the application of MEMS-IMU used in different fields. Focus has been on MEMS gyro since it is an essential and, yet, complex sensor in MEMS-IMU which is very sensitive to noises and errors from the random sources. In this study, recurrent neural networks are hybridized in four different ways for noise reduction and accuracy improvement in MEMS gyro. These are two-layer homogenous recurrent networks built on long short term memory (LSTM-LSTM) and gated recurrent unit (GRU-GRU), respectively, and another two-layer but heterogeneous deep networks built on long short term memory-gated recurrent unit (LSTM-GRU) and a gated recurrent unit-long short term memory (GRU-LSTM). Practical implementation with static and dynamic experiments was carried out for a custom MEMS-IMU to validate the proposed networks, and the results show that GRU-LSTM seems to be overfitting large amount data testing for three-dimensional axis gyro in the static test. However, for X-axis and Y-axis gyro, LSTM-GRU had the best noise reduction effect with over 90% improvement in the three axes. For Z-axis gyroscope, LSTM-GRU performed better than LSTM-LSTM and GRU-GRU in quantization noise and angular random walk, while LSTM-LSTM shows better improvement than both GRU-GRU and LSTM-GRU networks in terms of zero bias stability. In the dynamic experiments, the Hilbert spectrum carried out revealed that time-frequency energy of the LSTM-LSTM, GRU-GRU, and GRU-LSTM denoising are higher compared to LSTM-GRU in terms of the whole frequency domain. Similarly, Allan variance analysis also shows that LSTM-GRU has a better denoising effect than the other networks in the dynamic experiments. Overall, the experimental results demonstrate the effectiveness of deep learning algorithms in MEMS gyro noise reduction, among which LSTM-GRU network shows the best noise reduction effect and great potential for application in the MEMS gyroscope area.

In applications such as carrier attitude control and mobile device navigation, a micro-electro-mechanical-system (MEMS) gyroscope will inevitably be affected by random vibration, which significantly affects the performance of the MEMS gyroscope. In order to solve the degradation of MEMS gyroscope performance in random vibration environments, in this paper, a combined method of a long short-term memory (LSTM) network and Kalman filter (KF) is proposed for error compensation, where Kalman filter parameters are iteratively optimized using the Kalman smoother and expectation-maximization (EM) algorithm. In order to verify the effectiveness of the proposed method, we performed a linear random vibration test to acquire MEMS gyroscope data. Subsequently, an analysis of the effects of input data step size and network topology on gyroscope error compensation performance is presented. Furthermore, the autoregressive moving average-Kalman filter (ARMA-KF) model, which is commonly used in gyroscope error compensation, was also combined with the LSTM network as a comparison method. The results show that, for the x-axis data, the proposed combined method reduces the standard deviation (STD) by 51.58% and 31.92% compared to the bidirectional LSTM (BiLSTM) network, and EM-KF method, respectively. For the z-axis data, the proposed combined method reduces the standard deviation by 29.19% and 12.75% compared to the BiLSTM network and EM-KF method, respectively. Furthermore, for x-axis data and z-axis data, the proposed combined method reduces the standard deviation by 46.54% and 22.30% compared to the BiLSTM-ARMA-KF method, respectively, and the output is smoother, proving the effectiveness of the proposed method.

Sensor networks require a high degree of synchronization in order to produce a stream of data useful for further purposes. Examples of time misalignment manifest as undesired artifacts when doing multi-camera bundle-adjustment or global positioning system (GPS) geo-localization for mapping. Network Time Protocol (NTP) variants of clock synchronization can provide accurate results, though present high variance conditioned by the environment and the channel load. We propose a new precise technique for software clock synchronization over a network of rigidly attached devices using gyroscope data. Gyroscope sensors, or IMU, provide a high-rate measurements that can be processed efficiently. We use optimization tools over the correlation signal of IMU data from a network of gyroscope sensors. Our method provides stable microseconds accuracy, regardless of the number of sensors and the conditions of the network. In this paper, we show the performance of the gyroscope software synchronization in a controlled environment, and we evaluate the performance in a sensor network of smartphones by our open-source Android App, Twist-n-Sync, that is publicly available.

To reduce the impact of acoustic interference in a microelectromechanical system (MEMS) gyroscope and to improve the reliability of output data, a filtering algorithm based on orthogonal demodulation is proposed. According to the working principle and failure mechanism of a MEMS gyroscope, the sound and angular velocity frequencies are not identical, which lead to a different frequency signal output of the original single-channel demodulation scheme. Therefore, a Q channel demodulation filtering process was added to the origin single-channel demodulation scheme. For the Q channel demodulated signal, a Hilbert transform was used to compensate for the 90 degree phase shift. The IQ dual-channel difference can remove the acoustic interference signal. The simulation results indicate that the scheme can effectively suppress the acoustic interference signal and it can eliminate more than 95% of the impact of sound waves. We assembled the acoustic interference experimental platform, collected the driving and sensing data, and verified the denoising performance with our algorithm, which eliminated more than 70% of the noise signal. The simulation and experimental results demonstrate that the scheme can eliminate acoustic interference signal without destroying angular velocity signal.

This paper presents microfabrication process-driven design of a multi-degree of freedom (multi-DoF) non-resonant electrostatic microelectromechanical systems (MEMS) gyroscope by considering the design constraints of commercially available low-cost and widely-used silicon-on-insulator multi-user MEMS processes (SOIMUMPs), with silicon as a structural material. The proposed design consists of a 3-DoF drive mode oscillator with the concept of addition of a collider mass which transmits energy from the drive mass to the passive sense mass. In the sense direction, 2-DoF sense mode oscillator is used to achieve dynamically-amplified displacement in the sense mass. A detailed analytical model for the dynamic response of MEMS gyroscope is presented and performance characteristics are validated through finite element method (FEM)-based simulations. The effect of operating air pressure and temperature variations on the air damping and resulting dynamic response is analyzed. The thermal stability of the design and corresponding effect on the mechanical and capacitive sensitivity, for an operating temperature range of &minus, 40 &deg, C to 100 &deg, C, is presented. The results showed that the proposed design is thermally stable, robust to environmental variations, and process tolerances with a wide operational bandwidth and high sensitivity. Moreover, a system-level model of the proposed gyroscope and its integration with the sensor electronics is presented to estimate the voltage sensitivity under the constraints of the readout electronic circuit.

For the sake of decreasing the effects of noise and temperature error on the measurement accuracy of micro-electro-mechanical system (MEMS) gyroscopes, a denoising and temperature drift compensation parallel model method based on wavelet transform and forward linear prediction (WFLP) and support vector regression based on the cuckoo search algorithm (CS-SVR) is proposed in this paper. First, variational mode decomposition (VMD) is proposed in this paper, which is aimed at dividing the output signal of the gyroscope into intrinsic mode functions (IMFs), then, the IMFs are classified into three features&mdash, drift, mixed, and pure noise features&mdash, by the sample entropy (SE) value. Second, a wavelet transform and forward linear prediction (WFLP) are combined to remove the noise from the mixed features. Meanwhile, the drift feature is compensated by support vector regression based on the cuckoo search algorithm (CS-SVR). Finally, through reconstruction, the final signal is obtained. Experimental results demonstrate that the VMD-SE-WFLP-CS-SVR method proposed in this paper can decrease noise and compensate the temperature error effectively (angular random walking value is optimized from 1.667&deg, /&radic, h to 0.0667&deg, h and the bias stability is reduced from 30&deg, /h to 4&deg, /h). In terms of denoising, the performance of the WFLP algorithm is superior to the wavelet threshold and FLP, as it combines their advantages, furthermore, in terms of temperature compensation, the proposed CS-SVR algorithm uses the cuckoo search algorithm to find the optimal parameters of SVR, improving the accuracy of the model.

We report on a new design approach for X-Y symmetric resonator, emphasizing the increase in symmetry by localization of the device anchors. The resonator is mechanized as a z-axis rate gyroscope (RG), and the concept is compatible with the rate-integrating gyroscope mechanization. Our approach is based on strategically placing the critical mechanical elements at the center of the resonator structure, to mitigate the effect of fabrication imperfections across the device. We experimentally demonstrated that the design yields a sub-Hz frequency separation between operational modes, which is attributed to the introduced design approach. The high symmetry of the device led to a temperature stability of 50 mHz over 130 °C temperature range. A ring down test demonstrated the Q -factor greater than 330 K. The thermal stability and preliminary characterization results of the gyro in-run stability are presented. [2017-0177]

In a bid to solve the gyroscope temperature drift problem, a parallel denoising model based on PE-ITD and SA-ELM has been proposed in this paper, wherein, Intrinsic time-scale decomposition (ITD) is an effective signal decomposition algorithm, Permutation entropy (PE) is a entropy to accurately determine the complexity of the signal, Extreme Learning machine (ELM) is a machine learning algorithm for predicting, and Simulated Annealing(SA) for finding the optimal parameter set. First, ITD is employed to decompose the output signal of the gyroscope and the PE can distinguish the decomposition results into noise-only component, mixed component and trend component. Secondly, the mixed component is filtered by Forward liner prediction (FLP), the denoised signal can be obtained after processing. Finally, the trend component is compensated by SA-ELM, through reconstruction, the compensation result can be obtained. As is shown in experimental results, the parallel model proposed in this paper is able to effectively eliminate the temperature error compared with the traditional serial model. Compared with the EMD analysis method, the ITD is superior to the EMD analysis method in terms of computational efficiency and instantaneous information. The proposed SA-ELM algorithm takes simulated annealing algorithm to find hidden layer neurons' optimal number adaptively, which can improve the model's accuracy and generalization. This paper demonstrates the superiority of this method.

To deal with the influence of temperature drift for a Micro-Electro-Mechanical System (MEMS) gyroscope, this paper proposes a new temperature error parallel processing method based on a novel fusion algorithm. Firstly, immune based particle swarm optimization (IPSO) is employed for optimal parameters search for Variational Modal Decomposition (VMD). Then, we can get the optimal decomposition parameters, wherein permutation entropy (PE) is employed as the fitness function of the particles. Then, the improved VMD is performed on the output signal of the gyro to obtain intrinsic mode functions (IMFs). After judging by sample entropy (SE), the IMFs are divided into three categories: noise term, mixed term and feature term, which are processed differently. Filter the mixed term and compensate the feature term at the same time. Finally, reconstruct them and get the result. Compared with other optimization algorithms, IPSO has a stronger global search ability and faster convergence speed. After Back propagation neural network (BP) is enhanced by Adaptive boosting (Adaboost), it becomes a strong learner and a better model, which can approach the real value with higher precision. The experimental result shows that the novel parallel method proposed in this paper can effectively solve the problem of temperature errors.

MEMS gyroscopes can provide useful information for dead-reckoning navigation systems if suitable error compensation algorithm is applied. If there is information from other sources available, usually the Kalman filter is used for this task. This work focuses on improving the performance of the sensor if no other information is available and the integration error should be kept low during periods of still (no movement) operation. A filtering algorithm is proposed to follow bias change during sensor operation to reduce integration error and extend time between successive sensor calibrations. The advantage of the proposed solution is its low computational complexity which allows implementing it directly in the micro-controller of controlling the MEMS gyroscope. An intelligent sensor can be build this way, suitable for use in control systems for mobile platforms. Presented results of a simple experiment show the improvement of the angle estimation. During the 12 hours experiment with a common MEMS sensor and no thermal compensation, the maximum orientation angle error was below 8 degrees.

As an important inertial sensor, the gyroscope is mainly used to measure angular velocity in inertial space. However, due to the influence of semiconductor thermal noise and electromagnetic interference, the output of the gyroscope has a certain random noise and drift, which affects the accuracy of the detected angular velocity signal, thus interfering with the accuracy of the stability of the whole system. In order to reduce the noise and compensate for the drift of the MEMS (Micro Electromechanical System) gyroscope during usage, this paper proposes a Kalman filtering method based on information fusion, which uses the MEMS gyroscope and line accelerometer signals to implement the filtering function under the Kalman algorithm. The experimental results show that compared with the commonly used filtering methods, this method allows significant reduction of the noise of the gyroscope signal and accurate estimation of the drift of the gyroscope signal, and thus improves the control performance of the system and the stability accuracy.

International audience; This work consists of a new approach to identify and compensate the parasitic electrical coupling between the excitation and the detection circuits of a MEMS gyroscope. Based on multivariable identification and the nonlinear behavior of electrostatic actuators, we propose a systematic and flexible way to model the mechanical modes as well as the parasitic coupling. The electrical coupling model is then used to compensate the parasitic effects of the device. Our main contributions are: (i) no model structure is enforced, so all the dynamics between actuation and detection are considered; and (ii) the multivariable framework allows also identifying the parasitic cross-couplings.

This study presents a new microelectromechanical system, a vibration ring gyroscope with a double U-beam (DUVRG), which was designed using a combination of mathematical analysis and the finite element method. First, a ring vibration resonator with eight double U-beam structures was developed, and 24 capacitive electrodes were designed for drive and sense according to the advantageous characteristics of a thin-shell vibrating gyroscope. Then, based on the elastic mechanics and thin-shell theory, a mathematical stiffness model of the double U-beam was established. The maximum mode resonant frequency error calculated by the DUVRG stiffness model, finite element analysis (FEA) and experiments was 0.04%. DUVRG structures were manufactured by an efficient fabrication process using silicon-on-glass (SOG) and deep reactive ion etching (DRIE), and the FEA value and theoretical calculation had differences of 5.33% and 5.36% with the measured resonant frequency value, respectively. Finally, the static and dynamic performance of the fabricated DUVRG was tested, and the bias instability and angular random walk were less than 8.86 (&deg, )/h and 0.776 (&deg, )/&radic, h, respectively.

Research and industrial studies have indicated that small size, low cost, high precision, and ease of integration are vital features that characterize microelectromechanical systems (MEMS) inertial sensors for mass production and diverse applications. In recent times, sensors like MEMS accelerometers and MEMS gyroscopes have been sought in an increased application range such as medical devices for health care to defense and military weapons. An important limitation of MEMS inertial sensors is repeatedly documented as the ease of being influenced by environmental noise from random sources, along with mechanical and electronic artifacts in the underlying systems, and other random noise. Thus, random error processing is essential for proper elimination of artifact signals and improvement of the accuracy and reliability from such sensors. In this paper, a systematic review is carried out by investigating different random error signal processing models that have been recently developed for MEMS inertial sensor precision improvement. For this purpose, an in-depth literature search was performed on several databases viz., Web of Science, IEEE Xplore, Science Direct, and Association for Computing Machinery Digital Library. Forty-nine representative papers that focused on the processing of signals from MEMS accelerometers, MEMS gyroscopes, and MEMS inertial measuring units, published in journal or conference formats, and indexed on the databases within the last 10 years, were downloaded and carefully reviewed. From this literature overview, 30 mainstream algorithms were extracted and categorized into seven groups, which were analyzed to present the contributions, strengths, and weaknesses of the literature. Additionally, a summary of the models developed in the studies was presented, along with their working principles viz., application domain, and the conclusions made in the studies. Finally, the development trend of MEMS inertial sensor technology and its application prospects were presented.

The design, analysis, and simulation of a new Micro-electromechanical System (MEMS) gyroscope based on differential tunneling magnetoresistance sensing are presented in this paper. The device is driven by electrostatic force, whereas the Coriolis displacements are transferred to intensity variations of magnetic fields, further detected by the Tunneling Magnetoresistance units. The magnetic fields are generated by a pair of two-layer planar multi-turn copper coils that are coated on the backs of the inner masses. Together with the dual-mass structure of proposed tuning fork gyroscope, a two-stage differential detection is formed, thereby enabling rejection of mechanical and magnetic common-mode errors concurrently. The overall conception is described followed by detailed analyses of proposed micro-gyroscope and rectangle coil. Subsequently, the FEM simulations are implemented to determine the mechanical and magnetic characteristics of the device separately. The results demonstrate that the micro-gyroscope has a mechanical sensitivity of 1.754 nm/&deg, /s, and the micro-coil has a maximum sensitivity of 41.38 mOe/&micro, m. When the detection height of Tunneling Magnetoresistance unit is set as 60 &micro, m, the proposed device exhibits a voltage-angular velocity sensitivity of 0.131 mV/&deg, /s with a noise floor of 7.713 &times, 10&minus, 6&deg, /s/Hz in the absence of any external amplification.

This paper investigates one kind of high performance control methods for Micro-Electro-Mechanical-System (MEMS) gyroscopes using adaptive sliding mode control (ASMC) scheme with prescribed performance. Prescribed performance control (PPC) method is combined with conventional ASMC method to provide quantitative analysis of gyroscope tracking error performances in terms of specified tracking error bound and specified error convergence rate. The new derived adaptive prescribed performance sliding mode control (APPSMC) can maintain a satisfactory control performance which guarantees system tracking error, at any time, to be within a predefined error bound and the error convergences faster than the error bound. Besides, adaptive control (AC) technique is integrated with PPC to online tune controller parameters, which will converge to their true values at last. The stability of the control system is proved in the Lyapunov stability framework and simulation results on a Z-axis MEMS gyroscope is conducted to validate the effectiveness of the proposed control approach.

The gyro array is a useful technique in improving the accuracy of a micro-electro-mechanical system (MEMS) gyroscope, but the traditional estimate algorithm that plays an important role in this technique has two problems restricting its performance: The limitation of the stochastic assumption and the influence of the dynamic condition. To resolve these problems, a multi-model combined filter with dual uncertainties is proposed to integrate the outputs from numerous gyroscopes. First, to avoid the limitations of the stochastic and set-membership approaches and to better utilize the potentials of both concepts, a dual-noise acceleration model was proposed to describe the angular rate. On this basis, a dual uncertainties model of gyro array was established. Then the multiple model theory was used to improve dynamic performance, and a multi-model combined filter with dual uncertainties was designed. This algorithm could simultaneously deal with stochastic uncertainties and set-membership uncertainties by calculating the Minkowski sum of multiple ellipsoidal sets. The experimental results proved the effectiveness of the proposed filter in improving gyroscope accuracy and adaptability to different kinds of uncertainties and different dynamic characteristics. Most of all, the method gave the boundary surrounding the true value, which is of great significance in attitude control and guidance applications.

To improve the dynamic random error compensation accuracy of the Micro Electro Mechanical System (MEMS) gyroscope at different angular rates, an adaptive filtering approach based on the dynamic variance model was proposed. In this paper, experimental data were utilized to fit the dynamic variance model which describes the nonlinear mapping relations between the MEMS gyroscope output data variance and the input angular rate. After that, the dynamic variance model was applied to online adjustment of the Kalman Filter measurement noise coefficients. The proposed approach suppressed the interference from the angular rate in the filtering results. Dynamic random errors were better estimated and reduced. Turntable experiment results indicated that the adaptive filtering approach compensated for the MEMS gyroscope dynamic random error effectively both in the constant angular rate condition and the continuous changing angular rate condition, thus achieving adaptive dynamic random error compensation.

This paper presents the design and analysis of a multi degree of freedom (DOF) electro-thermally actuated non-resonant MEMS gyroscope with a 3-DOF drive mode and 1-DOF sense mode system. The 3-DOF drive mode system consists of three masses coupled together using suspension beams. The 1-DOF system consists of a single mass whose motion is decoupled from the drive mode using a decoupling frame. The gyroscope is designed to be operated in the flat region between the first two resonant peaks in drive mode, thus minimizing the effect of environmental and fabrication process variations on device performance. The high gain in the flat operational region is achieved by tuning the suspension beams stiffness. A detailed analytical model, considering the dynamics of both the electro-thermal actuator and multi-mass system, is developed. A parametric optimization is carried out, considering the microfabrication process constraints of the Metal Multi-User MEMS Processes (MetalMUMPs), to achieve high gain. The stiffness of suspension beams is optimized such that the sense mode resonant frequency lies in the flat region between the first two resonant peaks in the drive mode. The results acquired through the developed analytical model are verified with the help of 3D finite element method (FEM)-based simulations. The first three resonant frequencies in the drive mode are designed to be 2.51 kHz, 3.68 kHz, and 5.77 kHz, respectively. The sense mode resonant frequency is designed to be 3.13 kHz. At an actuation voltage of 0.2 V, the dynamically amplified drive mode gain in the sense mass is obtained to be 18.6 &micro, m. With this gain, a capacitive change of 28.11 &nbsp, f F and 862.13 &nbsp, f F is achieved corresponding to the sense mode amplitude of 0.15 &nbsp, &mu, m and 4.5 &nbsp, m at atmospheric air pressure and in a vacuum, respectively.

Under dynamic conditions, motion blur is introduced to star images obtained by a star sensor. Motion blur affects the accuracy of the star centroid extraction and the identification of stars, further reducing the performance of the star sensor. In this paper, a star image restoration algorithm is investigated to reduce the effect of motion blur on the star image. The algorithm includes a blur kernel calculation aided by a MEMS gyroscope, blur kernel correction based on the structure of the star strip, and a star image reconstruction method based on scaled gradient projection (SGP). Firstly, the motion trajectory of the star spot is deduced, aided by a MEMS gyroscope. Moreover, the initial blur kernel is calculated by using the motion trajectory. Then, the structure information star strip is extracted by Delaunay triangulation. Based on the structure information, a blur kernel correction method is presented by utilizing the preconditioned conjugate gradient interior point algorithm to reduce the influence of bias and installation deviation of the gyroscope on the blur kernel. Furthermore, a speed-up image reconstruction method based on SGP is presented for time-saving. Simulated experiment results demonstrate that both the blur kernel determination and star image reconstruction methods are effective. A real star image experiment shows that the accuracy of the star centroid extraction and the number of identified stars increase after restoration by the proposed algorithm.

This paper presents a study on dynamics of a dual-mass MEMS vibratory gyroscope in presence of fabrication imperfections and reports a method for precision electrostatic frequency tuning of the operational modes. A number of multi-mass MEMS gyroscopes have emerged in recent years pursuing different goals, such as dynamically balanced structure, increased bandwidth, and dynamic amplification. Along with many perceived advantages of multi-mass devices, several challenges associated with mode-matching in a system with increased number of degrees-of-freedom (DOF) have to be considered. This work shows that it is possible to apply the DC tuning techniques, similar to tuning a conventional single-mass gyroscope, to achieve the precision tuning in a dual-mass sensor, without losing advantages of increased DOF of the system. The presented frequency trimming technique is based on assessing the modes mismatch and cross-coupling between modes by means of fitting the experimental frequency response curves to the analytical solutions of the dual-mass system in presence of imperfections. The tuning algorithm involves two steps. First, the stiffness mismatch along the two axes and the anisoelasticity angles α and β are identified, then the tuning DC voltages for modification of diagonal, off-diagonal, and coupling terms in the stiffness matrix are chosen. The method of electrostatic tuning was validated through the experimental characterization of a dual-mass dynamically amplified gyroscope, where the coupling between the two operational modes was minimized and frequency split was reduced from 26 Hz down to 50 mHz, resulting in 17.5× increase in the gyroscope scale factor and significantly improved noise characteristics. The presented electrostatic compensation method is suitable for both off-line and on-line calibration.

Motion sensors such as MEMS gyroscopes and accelerometers are characterized by a small size, light weight, high sensitivity, and low cost. They are used in an increasing number of applications. However, they are easily influenced by environmental effects such as temperature change, shock, and vibration. Thus, signal processing is essential for minimizing errors and improving signal quality and system stability. The aim of this work is to investigate and present a systematic review of different signal error reduction algorithms that are used for MEMS gyroscope-based motion analysis systems for human motion analysis or have the potential to be used in this area. A systematic search was performed with the search engines/databases of the ACM Digital Library, IEEE Xplore, PubMed, and Scopus. Sixteen papers that focus on MEMS gyroscope-related signal processing and were published in journals or conference proceedings in the past 10 years were found and fully reviewed. Seventeen algorithms were categorized into four main groups: Kalman-filter-based algorithms, adaptive-based algorithms, simple filter algorithms, and compensation-based algorithms. The algorithms were analyzed and presented along with their characteristics such as advantages, disadvantages, and time limitations. A user guide to the most suitable signal processing algorithms within this area is presented.

A novel adaptive method for the micro-electro mechanical systems (MEMSs) gyroscope based on a dynamic surface control and combined with the approach of adaptive fuzzy and sliding mode control (SMC) was shown in this paper. The first-order filter was introduced to the conventional adaptive backstepping technique in the dynamic surface control, which not only maintains the advantage of original backstepping technique, but also reduces the number of parameters and avoids the problem of parameter expansion. The adaptive fuzzy logic system is employed to approximate the dynamic characteristics of the gyroscope; the SMC is a kind of compensation for the error of the fuzzy approximation, which reduced the chattering phenomenon in adaptive control significantly. The proposed control scheme has improved the dynamic characteristics of the gyroscope, reducing the chattering of inputs and improving the timeliness and effectiveness of tracking. Simulation results indicate that the proposed control scheme has superior performance compared with conventional SMC.

We present an adaptive algorithm for a system integrated with micro-electro-mechanical systems (MEMS) gyroscopes and a compass to eliminate the influence from the environment, compensate the temperature drift precisely, and improve the accuracy of the MEMS gyroscope. We use a simplified drift model and changing but appropriate model parameters to implement this algorithm. The model of MEMS gyroscope temperature drift is constructed mostly on the basis of the temperature sensitivity of the gyroscope. As the state variables of a strong tracking Kalman filter (STKF), the parameters of the temperature drift model can be calculated to adapt to the environment under the support of the compass. These parameters change intelligently with the environment to maintain the precision of the MEMS gyroscope in the changing temperature. The heading error is less than 0.6° in the static temperature experiment, and also is kept in the range from 5° to −2° in the dynamic outdoor experiment. This demonstrates that the proposed algorithm exhibits strong adaptability to a changing temperature, and performs significantly better than KF and MLR to compensate the temperature drift of a gyroscope and eliminate the influence of temperature variation.

Detta projekt syftar till att undersöka om det går att kurskorrigera en av Weda AB:s poolrengöringsrobotar med hjälp av ett MEMS-gyroskop. Den valda rengöringsroboten styrs idag av en PLC, och konceptet är att komplettera styrenheten med ett MEMS gyroskop som kan detektera kursavvikelser. Undersökandet av möjligheterna för implementeringen börjar med att samla information om MEMS gyroskopens funktion och i vilka tekniker de används idag. Vidare utförs beräkningar mot Wedas definition av rak kurs, det resulterade i för höga krav på känsligheten i gyroskopet. Trots det otillfredsställande resultatet beslutar Weda att projektet ska fortsätta undersöka gyroskopets möjligheter. En marknadsundersöknings med 9 tillverkningsföretag genomförs i syfte att se vad marknaden erbjuder för MEMS gyroskop idag. Sedan konsulteras distribueringsföretaget Avnet som bidrar med sina kunskaper, erfarenheter och rekommendationer för konceptet. Ett av de framstående gyroskopen i undersökningen användes för att genomföra två tester. Syftet av dem var att simulera olika situationer som rengöringsroboten kan utsättas för. Resultatet visar att repetitionsnoggrannheten är god men att gyroskopet inte är tillförlitligt i låga hastigheter, vilket är en dålig egenskap för rengöringsroboten W2000 som körs med en hastighet 0,2 m/s. Testresultatet diskuterades med Bosch och dem tror att gyroskopets automatiska kalibrering är orsaken till att det inte ger tillförlitliga mätdata vid låga hastigheter. During this project the possibilities to monitor and adjust the direction of a pool cleaning robot with a MEMS gyroscope are evaluated. The robot chosen for the evaluation is manufactured by Weda AB. The concept is to add a MEMS gyroscope to the robot, which will give the control unit (PLC) feedback regarding the direction of the robot. The evaluation includes general information about MEMS gyroscopes, where they are used, common built in errors etc. To be able to select a suitable MEMS gyroscope the needed sensitivity is calculated. The needed sensitivity is based on requirements from Weda AB. The evaluation also includes a market survey which shows that the needed sensitivity cannot be fulfilled by products available on the market. The market survey is based on nine manufacturing companies and a recommendation from a distribution company called Avnet. One of the prominent MEMS gyroscopes was used to carry out two tests. The tests were designed to mimic motions of the pool cleaning robot. The test result shows that the gyroscope has good repetition accuracy, but it is not trustworthy when the rotation speed is low. The test result was discussed with the manufacturer and they think that the non positive result is caused by the automatic calibration.

This paper presents a study of the frequency response and the scale-factor of a tuning fork micro-gyroscope operating at atmospheric pressure in the presence of an interference sense mode by utilizing the approximate transfer function. The optimal demodulation phase (ODP), which is always ignored in vacuum packaged micro-gyroscopes but quite important in gyroscopes operating at atmospheric pressure, is obtained through the transfer function of the sense mode, including the primary mode and the interference mode. The approximate transfer function of the micro-gyroscope is deduced in consideration of the interference mode and the ODP. Then, the equation describing the scale-factor of the gyroscope is also obtained. The impacts of the interference mode and Q-factor on the frequency response and the scale-factor of the gyroscope are analyzed through numerical simulations. The relationship between the scale-factor and the demodulation phase is also illustrated and gives an effective way to find out the ODP in practice. The simulation results predicted by the transfer functions are in close agreement with the results of the experiments. The analyses and simulations can provide constructive guidance on bandwidth and sensitivity designs of the micro-gyroscopes operating at atmospheric pressure.

In the following paper, we present an industry perspective of inertial sensors for navigation purposes driven by applications and customer needs. Microelectromechanical system (MEMS) inertial sensors have revolutionized consumer, automotive, and industrial applications and they have started to fulfill the high end tactical grade performance requirements of hybrid navigation systems on a series production scale. The Fiber Optic Gyroscope (FOG) technology, on the other hand, is further pushed into the near navigation grade performance region and beyond. Each technology has its special pros and cons making it more or less suitable for specific applications. In our overview paper, we present latest improvements at NG LITEF in tactical and navigation grade MEMS accelerometers, MEMS gyroscopes, and Fiber Optic Gyroscopes, based on our long-term experience in the field. We demonstrate how accelerometer performance has improved by switching from wet etching to deep reactive ion etching (DRIE) technology. For MEMS gyroscopes, we show that better than 1°/h series production devices are within reach, and for FOGs we present how limitations in noise performance were overcome by signal processing. The paper also intends a comparison of the different technologies, emphasizing suitability for different navigation applications, thus providing guidance to system engineers.

This work reports on the possibility to use gyroscopes based on piezoresistive NEMS gauges in vestibular implants. The specifications for this application are critically discussed, and used to derive parameters required at the sensor level and integrated circuit level. An algorithm to transform the acquired signals from the gyroscope reference system into the firing rate of vestibular nerves is also developed. The convenience in using gyroscopes based on nano-gauges lies in their possibility to operate at low voltages, compatible with implantable devices. A 3-axis gyroscope matching vestibular implant requirements is demonstrated at 0.35-mA, 2.8-V, without voltage boosting.

This paper presents a novel multi-objective parameter optimization method based on the genetic algorithm (GA) and adaptive moment estimation (Adam) algorithm for the design of a closed-loop control system for the sense mode of a Microelectromechanical systems (MEMS) gyroscope. The proposed method can improve the immunity of the control system to fabrication tolerances and external noise. The design procedure starts by deriving a parameterized model of the closed-loop of the sense mode. The loop parameters are then optimized by the GA. Finally, the ensemble of optimized loop parameters is tested by Monte Carlo analysis to obtain a robust optimal solution. Simultaneously, the Adam-least mean square (LMS) demodulator, which is appropriate for the demodulation of very noisy signals, is also presented. Compared with the traditional method, the time consumption of the design process is reduced significantly. The digital control system is implemented by the print circuit board based on embedded Field Programmable Gate Array (FPGA). The experimental results show that the optimized control loop has achieved a better performance, the system bandwidth in open-loop and optimal closed-loop control system is about 23 Hz and 101 Hz, respectively. Compared to a non-optimized closed-loop system, the bias instability reduced from 0.0015&deg, /s to 7.52 &times, 10&minus, 4&deg, /s, the scale factor increased from 17.7 mV/(&deg, /s) to 23 mV/(&deg, /s) and the non-linearity of the scale factor reduced from 0.008452% to 0.006156%.

This paper presents a bias drift self-calibration method for micro-electromechanical systems (MEMS) gyroscopes based on noise-suppressed mode reversal without the modeling of bias drift signal. At first, the bias drift cancellation is accomplished by periodic switching between operation mode of two collinear gyroscopes and subtracting the bias error which is estimated by the rate outputs from a consecutive period interval, then a novel filtering algorithm based on improved complete ensemble empirical mode decomposition (improved complete ensemble empirical mode decomposition with adaptive noise&mdash, CEEMDAN) is applied to eliminate the noise in the calibrated signal. A set of intrinsic mode functions (IMFs) is obtained by the decomposition of the calibrated signal using improved CEEMDAN method, and the threshold denoising method is utilized, finally, the de-noised IMFs are reconstructed into the desired signal. To verify the proposed method, the hardware circuit with an embedded field-programmable gate array (FPGA) was implemented and applied in bias drift calibration for the two MEMS gyroscopes manufactured in our laboratory. The experimental results indicate that the proposed method is feasible, and it achieved a better performance than the typical mode reversal. The bias instability of the two gyroscopes decreased from 0.0066 &deg, / s and 0.0055 &deg, / s to 0.0011 &deg, / s, and, benefiting from the threshold denoising based on improved CEEMDAN, the angle random walks decreased from 1.18 &times, 10 &minus, 4 &deg, / s 1 / 2 and 2.04 &times, / s 1 / 2 to 2.19 &times, 5 &deg, / s 1 / 2 , respectively.

The large random errors in Micro-Electro-Mechanical System (MEMS) gyros are one of the major factors that affect the precision of inertial navigation systems. Based on the indoor inertial navigation system, an improved wavelet threshold de-noising method was proposed and combined with a gradient radial basis function (RBF) neural network to better compensate errors. We analyzed the random errors in an MEMS gyroscope by using Allan variance, and introduced the traditional wavelet threshold methods. Then, we improved the methods and proposed a new threshold function. The new method can be used more effectively to detach white noise and drift error in the error model. Finally, the drift data was modeled and analyzed in combination with the RBF neural network. Experimental results indicate that the method is effective, and this is of great significance for improving the accuracy of indoor inertial navigation based on MEMS gyroscopes.

MEMS gyroscopes as a kind of angular rate sensor have been widely used, but their accuracy tends to be low in practical applications, especially under temperature influence, and they generally require error compensation. Based on the analysis of gyroscope operating principle, this paper has shown that the resonant frequency and measuring precision of the gyroscope are dependent on temperature and temperature gradients. The paper has thus proposed a compensation model based on temperature and temperature gradients. The experimental results have demonstrated that the thermal drift of zero bias can be effectively suppressed, and the accuracy can be improved by one order of magnitude after compensation. Compared with compensation methods only based on temperature, the new method gives significantly better performance. The new error compensation model has not only integrated the differences under different temperature conditions, but also reduced the repeatability errors. It provides a theoretical basis for accurate compensation of the gyroscope thermal error in practical applications and is applicable to other MEMS gyroscopes.

In a charge-coupled device (CCD)-based fast steering mirror (FSM) tracking control system, high control bandwidth is the most effective way to enhance the closed-loop performance. However, the control system usually suffers a great deal from mechanical resonances and time delays induced by the low sampling rate of CCDs. To meet the requirements of high precision and load restriction, fiber-optic gyroscopes (FOGs) are usually used in traditional FSM tracking control systems. In recent years, the MEMS accelerometer and gyroscope are becoming smaller and lighter and their performance have improved gradually, so that they can be used in a fast steering mirror (FSM) to realize the stabilization of the line-of-sight (LOS) of the control system. Therefore, a tentative approach to implement a CCD-based FSM tracking control system, which uses MEMS accelerometers and gyroscopes as feedback components and contains an acceleration loop, a velocity loop and a position loop, is proposed. The disturbance suppression of the proposed method is the product of the error attenuation of the acceleration loop, the velocity loop and the position loop. Extensive experimental results show that the MEMS accelerometers and gyroscopes can act the similar role as the FOG with lower cost for stabilizing the LOS of the FSM tracking control system.

The paper presents a two-step technique for real-time track detection in single-track railway sidings using low-cost MEMS gyroscopes. The objective is to reliably know the path the train has taken in a switch, diverted or main road, immediately after the train head leaves the switch. The signal delivered by the gyroscope is first processed by an adaptive low-pass filter that rejects noise and converts the temporal turn rate data in degree/second units into spatial turn rate data in degree/meter. The conversion is based on the travelled distance taken from odometer data. The filter is implemented to achieve a speed-dependent cut-off frequency to maximize the signal-to-noise ratio. Although direct comparison of the filtered turn rate signal with a predetermined threshold is possible, the paper shows that better detection performance can be achieved by processing the turn rate signal with a filter matched to the rail switch curvature parameters. Implementation aspects of the track detector have been optimized for real-time operation. The detector has been tested with both simulated data and real data acquired in railway campaigns.

MEMS gyroscope performs as a sort of sensor to detect angular velocity, with diverse applications in engineering including vehicle and intelligent traffic etc. A balanced vibration of driving module excited by electrostatic driving signal is the base MEMS gyroscope's performance. In order to analyze the linear property of vibration in MEMS Gyroscope, a method of computer vision measuring is applied with the help of high-speed vidicon to obtain video of linear vibration of driving module in gyroscope, under the driving voltage signal of inherent frequency and amplitude linearly increasing. By means of image processing, target identifying, and motion parameter extracting from the obtained video, vibration curve with time variation is acquired. And then, linearity of this vibration system can be analyzed by focusing on the amplitude value of vibration responding to the amplitude variation of driving voltage signal.

This paper presents an adaptive control approach for Micro-Electro-Mechanical Systems (MEMS) z-axis gyroscope sensor. The dynamical model of MEMS gyroscope sensor is derived and adaptive state tracking control for MEMS gyroscope is developed. The proposed adaptive control approaches can estimate the angular velocity and the damping and stiffness coefficients including the coupling terms due to the fabrication imperfection. The stability of the closed-loop systems is established with the proposed adaptive control strategy. Numerical simulation is investigated to verify the effectiveness of the proposed control scheme.

In order to solve the problem where existing mode-matching methods in microelectromechanical systems (MEMS) vibrating gyroscopes fail to meet real-time and reliability requirements, this paper presents a novel method to accomplish automatic and real-time mode-matching based on phase-shifted 45&deg, additional force demodulation (45&deg, AFD-RM). The phase-shifted 45&deg, additional force signal has the same frequency as the quadrature force signal, but it is phase-shifted by 45&deg, and applied to the sense mode. In addition, two-way phase-shifted 45&deg, demodulations are used at the sense-mode detection output to obtain a phase metric that is independent of the Coriolis force and can reflect the mode-matching state. Then, this phase metric is used as a control variable to adaptively control the tuning voltage, so as to change the sense-mode frequency through the negative stiffness effect and ultimately achieve real-time mode-matching. Simulation and experimental results show that the proposed 45&deg, AFD-RM method can achieve real-time matching. The mode frequency split is controlled within 0.1 Hz, and the gyroscope scale factor, zero-bias instability, and angle random walk are effectively improved.

This paper presents a two stage algorithm for real-time estimation of instanta- neous tremor parameters from gyroscope recordings. Gyroscopes possess the advantage of providing directly joint rotational speed, overcoming the limitations of traditional tremor recording based on accelerometers. The proposed algorithm first extracts tremor patterns from raw angular data, and afterwards estimates its instantaneous amplitude and frequency. Real-time separation of voluntary and tremorous motion relies on their different frequency contents, whereas tremor modelling is based on an adaptive LMS algorithm and a Kalman filter. Tremor parameters will be employed to drive a neuroprosthesis for tremor suppression based on biomechanical loading. The work presented in this paper has been carried out with the financial support from the Commission of the European Union, within Framework 7, specific IST programme “Accessible and Inclusive ICT”, Target outcome 7.2 “Advanced self-adaptive ICT-enabled assistive systems based on non-invasive Brain to Computer Interaction (BCI)”, under Grant Agreement number ICT-2007-224051, “TREMOR: An ambulatory BCI-driven tremor suppression system based on functional electrical stimulation.”