Showing papers on "Gyroscope published in 2017"
TL;DR: In recent years, micro-machined electromechanical system inertial sensors (3D accelerometers and 3D gyroscopes) have become widely available due to their small size and low cost.
Abstract: In recent years, MEMS inertial sensors (3D accelerometers and 3D gyroscopes) have become widely available due to their small size and low cost. Inertial sensor measurements are obtained at high sampling rates and can be integrated to obtain position and orientation information. These estimates are accurate on a short time scale, but suffer from integration drift over longer time scales. To overcome this issue, inertial sensors are typically combined with additional sensors and models. In this tutorial we focus on the signal processing aspects of position and orientation estimation using inertial sensors. We discuss different modeling choices and a selected number of important algorithms. The algorithms include optimization-based smoothing and filtering as well as computationally cheaper extended Kalman filter and complementary filter implementations. The quality of their estimates is illustrated using both experimental and simulated data.
304 citations
TL;DR: This paper is an overview of current gyroscopes and their roles based on their applications, and gyroscope technologies commercially available, such as Mechanical Gyroscope, silicon MEMS Gyroscopes, Ring Laser Gyroscope (RLGs) and Fiber-OpticGyroscopes (FOGs), are discussed.
Abstract: This paper is an overview of current gyroscopes and their roles based on their applications. The considered gyroscopes include mechanical gyroscopes and optical gyroscopes at macro- and micro-scale. Particularly, gyroscope technologies commercially available, such as Mechanical Gyroscopes, silicon MEMS Gyroscopes, Ring Laser Gyroscopes (RLGs) and Fiber-Optic Gyroscopes (FOGs), are discussed. The main features of these gyroscopes and their technologies are linked to their performance.
287 citations
20 Mar 2017
TL;DR: In this paper, a chip-based, micro-optical gyroscope is demonstrated that uses counterpropagating Brillouin lasers to measure rotation as a Sagnac-induced frequency shift.
Abstract: Optical-based rotation sensors have revolutionized precision, high-sensitivity inertial navigation systems. At the same time these sensors use bulky optical fiber spools or free-space resonators. A chip-based, micro-optical gyroscope is demonstrated that uses counterpropagating Brillouin lasers to measure rotation as a Sagnac-induced frequency shift. Preliminary work has demonstrated a rotation-rate measurement that surpasses prior micro-optical rotation-sensing systems by over 40-fold.
211 citations
20 Jan 2017
TL;DR: In this paper, an integrated passive gyroscope using a monolithic cavity characterized by 7 mm diameter, finesse of 105, and Rayleigh backscattering less than 10 ppm is presented.
Abstract: It has been known for four decades that high finesse millimeter-scale optical cavities can produce high-performance, compact optical gyroscopes. Yet, a practical implementation of such a device has been hindered by multiple technical challenges, including Rayleigh scattering and optical nonlinearity of the cavity material. In this Letter we report on the implementation of an integrated passive gyroscope using a monolithic cavity characterized by 7 mm diameter, finesse of 105, and Rayleigh backscattering less than 10 ppm. The device is characterized with quantum noise limited angle random walk of 0.02 deg/h1/2, and bias drift of 3 deg/h, corresponding to detection of rotation-originated optical path change of 1.3×10−16 cm.
144 citations
TL;DR: It is shown that vehicle kinematics allow the removal of external accelerations from the lateral and vertical axis accelerometer measurements, thus giving the correct estimate of lateral and Vertical axis gravitational accelerations.
Abstract: This paper presents a novel Kalman filter for the accurate determination of a vehicle’s attitude (pitch and roll angles) using a low-cost MEMS inertial measurement unit (IMU) sensor, comprising a tri-axial gyroscope and a tri-axial accelerometer. Currently, vehicles deploy expensive gyroscopes for attitude determination. A low-cost MEMS gyro cannot be used because of the drift problem. Typically, an accelerometer is used to correct this drift by measuring the attitude from gravitational acceleration. This is, however, not possible in vehicular applications, because accelerometer measurements are corrupted by external accelerations produced due to vehicle movements. In this paper, we show that vehicle kinematics allow the removal of external accelerations from the lateral and vertical axis accelerometer measurements, thus giving the correct estimate of lateral and vertical axis gravitational accelerations. An estimate of the longitudinal axis gravitational acceleration can then be obtained by using the vector norm property of gravitational acceleration. A Kalman filter is designed, which implements the proposed solution and uses the accelerometer in conjunction with the gyroscope to accurately determine the attitude of a vehicle. Hence, this paper enables the use of extremely low-cost MEMS IMU for accurate attitude determination in vehicular domain for the first time. The proposed filter was tested by both simulations and experiments under various dynamic conditions and results were compared with five existing methods from the literature. The proposed filter was able to maintain sub-degree estimation accuracy even under very severe and prolonged dynamic conditions. To signify the importance of the achieved accuracy in determining accurate attitude, we investigated its use in two vehicular applications: vehicle yaw estimate and vehicle location estimate by dead reckoning and showed the performance improvements obtained by the proposed filter.
125 citations
TL;DR: In this paper, the authors proposed a new scheme for ultra-sensitive laser gyroscopes that utilizes the physics of exceptional points and showed that the rotation-induced frequency splitting becomes proportional to the square root of the gyration speed, thus enhancing the sensitivity to low angular rotations by orders of magnitudes.
Abstract: We propose a new scheme for ultrasensitive laser gyroscopes that utilizes the physics of exceptional points. By exploiting the properties of such non-Hermitian degeneracies, we show that the rotation-induced frequency splitting becomes proportional to the square root of the gyration speed (Ω), thus enhancing the sensitivity to low angular rotations by orders of magnitudes. In addition, at its maximum sensitivity limit, the measurable spectral splitting is independent of the radius of the rings involved. This Letter paves the way toward a new class of ultrasensitive miniature ring laser gyroscopes on chip.
100 citations
TL;DR: In this article, a 3D symmetric triboelectric nanogenerator-based gyroscope ball (T-ball) with dual capability of energy harvesting and self-powered sensing is proposed for motion monitoring including multiaxis acceleration and rotation.
Abstract: Healthcare monitoring systems can provide important health state information by monitoring the biomechanical parameter or motion of body segments. Triboelectric nanogenerators (TENGs) as self-powered motion sensors have been developed rapidly to convert external mechanical change into electrical signal. However, research effort on using TENGs for multiaxis acceleration sensing is very limited. Moreover, TENG has not been demonstrated for rotation sensing to date. Herein, for the first time, a 3D symmetric triboelectric nanogenerator-based gyroscope ball (T-ball) with dual capability of energy harvesting and self-powered sensing is proposed for motion monitoring including multiaxis acceleration and rotation. The T-ball can harvest energy under versatile scenarios and function as self-powered 3D accelerometer with sensitivity of 6.08, 5.87, and 3.62 V g−1 . Furthermore, the T-ball can serve as a self-powered gyroscope for rotation sensing with sensitivity of 3.5 mV so−1. It shows good performance in hand motion recognition and human activity state monitoring applications. The proposed T-ball as a self-powered gyroscope for advanced motion sensing can pave the way to a self-powered, more accurate, and more complete motion monitoring system.
92 citations
TL;DR: In this paper, a low-cost tri-axial MEMS accelerometer and a gyroscope is used to calibrate an inertial measurement unit (IMU) comprised of a low cost tri-axis accelerometer, which utilizes gravity signal as a stable reference.
Abstract: Recently, micro electro-mechanical systems (MEMS) inertial sensors have found their way in various applications. These sensors are fairly low cost and easily available but their measurements are noisy and imprecise, which poses the necessity of calibration. In this paper, we present an approach to calibrate an inertial measurement unit (IMU) comprised of a low-cost tri-axial MEMS accelerometer and a gyroscope. As opposed to existing methods, our method is truly infield as it requires no external equipment and utilizes gravity signal as a stable reference. It only requires the sensor to be placed in approximate orientations, along with the application of simple rotations. This also offers easier and quicker calibration comparatively. We analyzed the method by performing experiments on two different IMUs: an in-house built IMU and a commercially calibrated IMU. We also calibrated the in-house built IMU using an aviation grade rate table for comparison. The results validate the calibration method as a useful low-cost IMU calibration scheme.
90 citations
01 Jan 2017
TL;DR: It is shown that the gyroscope bias, not accounted for in [1], significantly affects the performance of the closed-form solution and a new method is introduced to automatically estimate this bias and is robust to it.
Abstract: State of the art approaches for visual-inertial sensor fusion use filter-based or optimization-based algorithms. Due to the nonlinearity of the system, a poor initialization can have a dramatic impact on the performance of these estimation methods. Recently, a closed-form solution providing such an initialization was derived in [1] . That solution determines the velocity (angular and linear) of a monocular camera in metric units by only using inertial measurements and image features acquired in a short time interval. In this letter, we study the impact of noisy sensors on the performance of this closed-form solution. We show that the gyroscope bias, not accounted for in [1] , significantly affects the performance of the method. Therefore, we introduce a new method to automatically estimate this bias. Compared to the original method, the new approach now models the gyroscope bias and is robust to it. The performance of the proposed approach is successfully demonstrated on real data from a quadrotor MAV.
90 citations
TL;DR: Wang et al. as discussed by the authors presented a wearable inertial pedestrian navigation system and its associated pedestrian trajectory reconstruction algorithm for reconstructing pedestrian walking trajectories in indoor and outdoor environments, which consists of the procedures of inertial signal acquisition, signal preprocessing, trajectory reconstruction, and trajectory height estimation.
Abstract: This paper presents a wearable inertial pedestrian navigation system and its associated pedestrian trajectory reconstruction algorithm for reconstructing pedestrian walking trajectories in indoor and outdoor environments. The proposed wearable inertial pedestrian navigation system is constructed by integrating a triaxial accelerometer, a triaxial gyroscope, a triaxial magnetometer, a microcontroller, and a Bluetooth wireless transmission module. Users wear the system on foot while walking in indoor and outdoor environments at normal speed without any external positioning techniques. During walking movement, the measured inertial signals generated from walking movements are transmitted to a computer via the wireless module. Based on the foot-mounted inertial pedestrian navigation system, a pedestrian trajectory reconstruction algorithm composed of the procedures of inertial signal acquisition, signal preprocessing, trajectory reconstruction, and trajectory height estimation has been developed to reconstruct floor walking and stair climbing trajectories. In order to minimize the cumulative error of the inertial signals, we have utilized a sensor fusion technique based on a double-stage quaternion-based extended Kalman filter to fuse acceleration, angular velocity, and magnetic signals. Experimental results have successfully validated the effectiveness of the proposed wearable inertial pedestrian navigation system and its associated pedestrian trajectory reconstruction algorithm.
76 citations
TL;DR: In this article, the authors consider the malicious effects that inhomogeneous magnetic fields, which are often found in indoor environments, can have on the inclination portions (roll and pitch) of the orientation estimate and propose a novel method that uses an analytical solution of the sensor fusion problem and purely horizontal magnetometer-based corrections.
TL;DR: In this article, the direction of reference signals, the gravity and the magnetic field of the Earth, are estimated with calibration parameters for consumer-grade accelerometers, gyroscopes, and magnetometers.
Abstract: This paper presents a calibration method for consumer-grade accelerometers, gyroscopes, and magnetometers. Considering the calibration of consumer-grade sensors, it is essential that no specialized equipment is required to create reference signals. In addition, the less is required from the reference signals, the more suitable the method is on the field. In the proposed method, the novelty in the calibration of the gyroscopes lies in the exploitation of only the known net rotations between the positions in a multi-position calibration. For accelerometers and magnetometers, the innovation is that the direction of reference signals, the gravity and the magnetic field of the Earth, are estimated with calibration parameters. As a consequence, no precise absolute alignment of the sensors is needed in the calibration. The rotations need not be done about a constant axis. In the proposed method, the biases, scale factors, misalignments, and cross-coupling errors for all the sensors as well as hard iron and soft iron effect for magnetometers were modelled. In addition, the drift of the sensors during the calibration was estimated. As a result, all the sensors were calibrated at once to the same frame, exploiting only a cube and a jig and thus, the method is eligible in the field. To estimate the quality of the calibration results, 95% confidence intervals were calculated for the calibration parameters. Simulations were done to indicate that the calibration method is unbiased.
TL;DR: The proposed approach estimates the direction of linear acceleration and assigns lower weights inside the Kalman filter to only those sensor axes that are experiencing acceleration, thus conserving important information from other axes measurements.
Abstract: Accurately estimating the orientation of different human body segments using low cost inertial sensors is a key component in various activity-related and healthcare-related applications. Typically, the signals from a gyroscope and an accelerometer are fused inside a Kalman filter to determine the orientation. However, the accelerometer measurements are influenced by the linear accelerations of the body segments in addition to the gravitational acceleration that corrupts the orientation estimates. The conventional method to deal with linear acceleration is to model it as a first-order low-pass process and estimate it inside the Kalman filter. In this conventional method, important information from those sensor axes that do not experience linear accelerations is lost. In this paper, we modify the conventional approach to deal with the problem of linear acceleration more efficiently. The proposed approach estimates the direction of linear acceleration and assigns lower weights inside the Kalman filter to only those sensor axes that are experiencing acceleration, thus conserving important information from other axes measurements. The proposed method is compared with the conventional method using simulations and experimentation on a test subject performing daily routine tasks. The results indicate a significant performance improvement in orientation estimation.
TL;DR: A methodology to achieve better estimates of physical values by processing raw measurements within a sensor using multi-physical models and Kalman filters for data fusion and improving both precision and reliability despite low power processors limitations is established.
Abstract: Over the last decade, smart sensors have grown in complexity and can now handle multiple measurement sources. This work establishes a methodology to achieve better estimates of physical values by processing raw measurements within a sensor using multi-physical models and Kalman filters for data fusion. A driving constraint being production cost and power consumption, this methodology focuses on algorithmic complexity while meeting real-time constraints and improving both precision and reliability despite low power processors limitations. Consequently, processing time available for other tasks is maximized. The known problem of estimating a 2D orientation using an inertial measurement unit with automatic gyroscope bias compensation will be used to illustrate the proposed methodology applied to a low power STM32L053 microcontroller. This application shows promising results with a processing time of 1.18 ms at 32 MHz with a 3.8% CPU usage due to the computation at a 26 Hz measurement and estimation rate.
TL;DR: In this article, it is shown that the sensitivity of frequency splitting can be significantly enhanced in a ring resonator operating at an exceptional point (EP), which is a non-Hermitian degeneracy where two eigenvalues and the corresponding eigenmodes coalesce.
Abstract: In rotating ring resonators, resonant frequencies are split because of the Sagnac effect. The rotation sensitivity of the frequency splitting characterizes the sensitivity of resonator-based optical gyroscopes. In this paper, it is shown that the sensitivity of frequency splitting can be significantly enhanced in a ring resonator operating at an exceptional point (EP), which is a non-Hermitian degeneracy where two eigenvalues and the corresponding eigenmodes coalesce. As an example, a ring resonator with a periodic structure is proposed and theoretically and numerically studied. It is numerically demonstrated that in the resonator operating near an EP, the rotation-induced frequency splitting can be more than two orders greater than that in conventional ring resonators. In addition, this paper discusses the influence of the resonator loss on the measurement sensitivity of the frequency splitting and a method of rotation detection based on rotation-induced changes of eigenmodes near an EP.
TL;DR: In this article, the authors consider the problem of controlling an aerial robot connected to the ground by a passive cable or a passive rigid link, and provide a thorough characterization of this nonlinear dynamical robotic system in terms of fundamental properties such as differential flatness, controllability, and observability.
Abstract: In this paper, we consider the problem of controlling an aerial robot connected to the ground by a passive cable or a passive rigid link. We provide a thorough characterization of this nonlinear dynamical robotic system in terms of fundamental properties such as differential flatness, controllability, and observability. We prove that the robotic system is differentially flat with respect to two output pairs: elevation of the link and attitude of the vehicle; elevation of the link and longitudinal link force (e.g., cable tension, or bar compression). We show the design of an almost globally convergent nonlinear observer of the full state that resorts only to an onboard accelerometer and a gyroscope. We also design two almost globally convergent nonlinear controllers to track any sufficiently smooth time-varying trajectory of the two output pairs. Finally, we numerically test the robustness of the proposed method in several far-from-nominal conditions: nonlinear cross-coupling effects, parameter deviations, measurements noise, and nonideal actuators.
TL;DR: In this paper, the development and evaluation of passive optical ring resonator gyroscopes (OPRGs) are critical reviewed and the countermeasures against the parasitic noise sources which are encountered in the OPRG including backscattering, backreflection, polarization error, nonlinear Kerr effect, and laser frequency noise.
Abstract: In this paper, the development and evaluation of passive optical ring resonator gyroscopes (OPRGs) are critical reviewed. Attention has been paid to the countermeasures against the parasitic noise sources which are encountered in the OPRG including backscattering, backreflection, polarization error, nonlinear Kerr effect, and laser frequency noise. The new progress in a feasible OPRG is shown. In addition, the advanced and complex digital signal processing in the OPRGs is also addressed.
TL;DR: In this article, a low-cost multisensor integrated navigation system was developed for a quadrotor aerial vehicle (QAV), which integrates a global positioning system (GPS) module, triaxial gyroscope, accelerometer, magnetometer, and digital barometer.
Abstract: The performance of navigation sensors may be seriously affected by the operating rotors of a drone. To address this kind of disturbance, a low-cost multisensor integrated navigation system was developed for a quadrotor aerial vehicle (QAV). The navigation board integrates a global positioning system (GPS) module, triaxial gyroscope, accelerometer, magnetometer, and a digital barometer. The sensors’ outputs were mathematically modeled and subsequently used in the integration Kalman filter. The data processing consisted of several steps: prefiltering, centralized filtering, and feedback. On the basis of onboard tests, the stochastic models of sensors were established in the presence of vibration, revolution, and ventilation caused by the QAV rotor’s operation, and in particular, its electromagnetic and aerodynamic characteristics. Flight experiments indicate that the proposed integrated navigation system can significantly improve flight accuracy and reliability.
TL;DR: A novel sensor fusion approach using Ultra Wide Band (UWB) wireless radio and an Inertial Navigation System (INS), which aims to reduce the accumulated error of low-cost Micro-Electromechanical systems used for real-time navigation and tracking of mobile robots in a closed environment.
Abstract: This paper proposes a novel sensor fusion approach using Ultra Wide Band (UWB) wireless radio and an Inertial Navigation System (INS), which aims to reduce the accumulated error of low-cost Micro-Electromechanical Systems (MEMS) Inertial Navigation Systems used for real-time navigation and tracking of mobile robots in a closed environment. A tightly-coupled model of INS/UWB is established within the integrated positioning system. A two-dimensional kinematic model of the mobile robot based on kinematics analysis is then established, and an Auto-Regressive (AR) algorithm is used to establish third-order error equations of the gyroscope and the accelerometer. An Improved Adaptive Kalman Filter (IAKF) algorithm is proposed. The orthogonality judgment method of innovation is used to identify the “outliers”, and a covariance matching technique is introduced to judge the filter state. The simulation results show that the IAKF algorithm has a higher positioning accuracy than the KF algorithm and the UWB system. Finally, static and dynamic experiments are performed using an indoor experimental platform. The results show that the INS/UWB integrated navigation system can achieve a positioning accuracy of within 0·24 m, which meets the requirements for practical conditions and is superior to other independent subsystems.
TL;DR: In this article, the authors consider the calibration of an optical quantum gyroscope by modeling two Sagnac interferometers, mounted approximately at right angles to each other, and find that the optimum level of entanglement depends strongly on the sought parameter values.
Abstract: We consider the calibration of an optical quantum gyroscope by modeling two Sagnac interferometers, mounted approximately at right angles to each other. Reliable operation requires that we know the angle between the interferometers with high precision, and we show that a procedure akin to multiposition testing in inertial navigation systems can be generalized to the case of quantum interferometry. We find that while entanglement is a key resource within an individual Sagnac interferometer, its presence between the interferometers is a far more complicated story. The optimum level of entanglement depends strongly on the sought parameter values, and small but significant improvements may be gained from choosing states with the optimal amount of entanglement between the interferometers.
TL;DR: To the best of the knowledge, this is the first RMOG reported in the open literature that can sense the earth's rotation rate (15°/h) and a bias stability of 0.004°/s is observed over a 1 h timeframe.
Abstract: A new record for high-finesse silica waveguide ring resonators (WRRs), to the best of our knowledge, is demonstrated experimentally. The achieved finesse and resonant depths of the silica WRR with a length of 7.9 cm and a diameter of 2.5 cm are 196.7% and 98%, respectively. In addition, the silica WRR chip is coupled with single-polarization fiber to improve the polarization extinction ratio (PER) and, thus, to reduce the polarization error. With the application of this high-finesse and high-PER WRR to the resonant micro-optic gyroscope (RMOG), a bias stability of 0.004°/s is observed over a 1 h timeframe. To the best of our knowledge, this is the first RMOG reported in the open literature that can sense the earth’s rotation rate (15°/h).
TL;DR: Wavelet filtering is proposed to use wavelet filtering to reduce noise in the original data of MEMS gyroscopes, then reconstruct the random drift data with PSR (phase space reconstruction), and establish the model for the reconstructed data by LSSVM (least squares support vector machine), of which the parameters were optimized using CPSO (chaotic particle swarm optimization).
Abstract: MEMS (Micro Electro Mechanical System) gyroscopes have been widely applied to various fields, but MEMS gyroscope random drift has nonlinear and non-stationary characteristics. It has attracted much attention to model and compensate the random drift because it can improve the precision of inertial devices. This paper has proposed to use wavelet filtering to reduce noise in the original data of MEMS gyroscopes, then reconstruct the random drift data with PSR (phase space reconstruction), and establish the model for the reconstructed data by LSSVM (least squares support vector machine), of which the parameters were optimized using CPSO (chaotic particle swarm optimization). Comparing the effect of modeling the MEMS gyroscope random drift with BP-ANN (back propagation artificial neural network) and the proposed method, the results showed that the latter had a better prediction accuracy. Using the compensation of three groups of MEMS gyroscope random drift data, the standard deviation of three groups of experimental data dropped from 0.00354°/s, 0.00412°/s, and 0.00328°/s to 0.00065°/s, 0.00072°/s and 0.00061°/s, respectively, which demonstrated that the proposed method can reduce the influence of MEMS gyroscope random drift and verified the effectiveness of this method for modeling MEMS gyroscope random drift.
TL;DR: This work presents the first chip-scale "integrated optical driver" (IOD) that can interrogate with a sensing coil to realize an interferometric optical gyroscope.
Abstract: We present the first chip-scale "integrated optical driver" (IOD) that can interrogate with a sensing coil to realize an interferometric optical gyroscope. The chip comprises a light source, three photodiodes, two phase modulators and two 3-dB couplers within an area of 4.5 mm2. This allows for a significant reduction in size, weight, power consumption and cost of optical gyroscopes.
TL;DR: In this article, the authors proposed a configuration of two ring laser arrays, one at maximum signal and the other at horizontal, to measure the Lense-Thirring (LT) and de Sitter effects.
Abstract: GINGER (Gyroscopes IN General Relativity) is a proposal for an Earth-based experiment to measure the Lense-Thirring (LT) and de Sitter effects. GINGER is based on ring lasers, which are the most sensitive inertial sensors to measure the rotation rate of the Earth. We show that two ring lasers, one at maximum signal and the other horizontal, would be the simplest configuration able to retrieve the GR effects. Here, we discuss this configuration in detail showing that it would have the capability to test LT effect at 1%, provided the accuracy of the scale factor of the instrument at the level of 1 part in 1012 is reached. In principle, one single ring laser could do the test, but the combination of the two ring lasers gives the necessary redundancy and the possibility to verify that the systematics of the lasers are sufficiently small. The discussion can be generalised to seismology and geodesy and it is possible to say that signals 10-12 orders of magnitude below the Earth rotation rate can be studied; the proposed array can be seen as the basic element of multi-axial systems, and the generalisation to three dimensions is feasible adding one or two devices and monitoring the relative angles between different ring lasers. This simple array can be used to measure with very high precision the amplitude of angular rotation rate (the length of the day, LOD), its short term variations, and the angle between the angular rotation vector and the horizontal ring laser. Finally this experiment could be useful to probe gravity at fundamental level giving indications on violations of Einstein Equivalence Principle and Lorenz Invariance and possible chiral effects in the gravitational field.
TL;DR: Having several faulty modes due to the possibility of single and multiple failures in the sensors, it is proposed in this paper to decompose the system to interacting observable subsystems with reduced size and decoupled model sets using a graph theoretic decomposition approach.
Abstract: In this paper, a distributed interacting multiple model-based fault detection and isolation (FDI) scheme is presented for FDI of navigation sensors composed of inertial (accelerometers and gyroscopes) and camera sensors in a robotic system in which noisy and erroneous measurements of microelectro mechanical system (MEMS)-based inertial sensors are fused with a photogrammetric camera. Multiple models are employed to describe different scenarios of hard faults (failures) in the sensors where the models are different in each scenario because inertial sensor drifts (as soft or partial faults) are also modeled and augmented to the motion state parameters. Having several faulty modes due to the possibility of single and multiple failures in the sensors, it is proposed in this paper to decompose the system to interacting observable subsystems with reduced size and decoupled model sets. The system and the corresponding model set are decomposed using a graph theoretic decomposition approach. Distributed interacting multiple Kalman and extended Kalman filters are then designed for the purpose of FDI. Experimental results based on data from a 3-D MEMS inertial measurement unit and 3-D camera system are used to demonstrate the efficiency of the method.
01 Sep 2017
TL;DR: A fault detection algorithm based on the innovation vector is added to the EKF system to effectively detect and eliminate the gross errors in the measurements, to improve the filtering effect of EKf algorithm, and ensure the accuracy of pedestrian navigation results.
Abstract: A foot-mounted pedestrian dead reckoning system is a self-contained technique for indoor localization. An inertial pedestrian navigation system includes wearable MEMS inertial sensors, such as an accelerometer, gyroscope, barometer, or magnetometer, which enable the measurement of the step length and the heading direction. In this plan, a method based on IMU/EKF+HMM+ZUPT+ZARU+HDR+the Earth Magnetic Yaw was designed to realize foot-mounted pedestrian navigation. Based on the characteristics of pedestrian navigation, the general likelihood ratio test (GLRT) and the Hidden Markov Model (HMM) were used to realize the detection of zero speed interval at different speed states. When the zero speed state is detected, the zero velocity update (ZUPT) method is used to limit the accumulation of IMU. The Zero Angular Rate Update (ZARU) + (heuristic heading reduction) HDR+the Earth Magnetic Yaw method is used to limit the IMU attitude and heading drift. Finally, the EKF method is used to realize the effective estimation and feedback of the speed, attitude and heading error of the pedestrian navigation system. Meanwhile, a fault detection algorithm based on the innovation vector is added to the EKF system to effectively detect and eliminate the gross errors in the measurements, to improve the filtering effect of EKF algorithm, and ensure the accuracy of pedestrian navigation results.
TL;DR: In this paper, a silicon-on-insulator matched-mode z-axis vibratory-rate gyroscope was used as a prototype testbed to study the effects of on-chip stress sensors.
Abstract: Long-term gyroscope drift can be effectively removed by employing simultaneous on-chip stress and temperature compensation. Stress effects are significant and their inclusion augments the commonly applied temperature compensation. A silicon-on-insulator matched-mode z-axis vibratory-rate gyroscope, as a prototype testbed to study these effects, includes released silicon resistors connected in a Wheatstone bridge as on-chip stress sensors. The gyroscope is ovenized within 300 K ± 20 mK using an external heater and an on-chip temperature sensor to suppress the temperature effects. The gyroscope is in-house vacuum packaged and operated at matched closed-loop drive and sense modes. Stress compensation significantly suppresses long-term drift resulting in 9°/h/√Hz angle random walk and 1°/h bias instability at 10000 s (around 3 h) averaging time, which is seven times improvement over the uncompensated gyroscope output. The sensitivity of zero-rate offset to stress is -0.22°/day/Pa and -0.045°/day/Pa for the tests with and without externally applied stress, respectively.
TL;DR: An industry perspective of inertial sensors for navigation purposes driven by applications and customer needs is presented, and a comparison of the different technologies is intended, emphasizing suitability for different navigation applications, thus providing guidance to system engineers.
Abstract: 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.
TL;DR: In this article, a utility algorithm was proposed to reduce the permanent frequency mismatch of the primary wineglass modes in a disk resonator gyroscope via stiffness perturbation, where each tuning electrode is regarded as two rows of radial springs with negative stiffness.
Abstract: This paper introduces a utility algorithm to reduce the permanent frequency mismatch of the primary wineglass modes in a disk resonator gyroscope via stiffness perturbation. In this algorithm, each tuning electrode is regarded as two rows of radial springs with negative stiffness. On the basis of the tuning model for a ring with radial springs, we can obtain the functional relation between equivalent stiffness or voltage of each tuning electrode and the frequency split. Corresponding simulations are conducted to demonstrate the performance of the algorithm. Finally, the electrostatic tuning experiment is presented to further verify the algorithm and illustrate the use of the tuning procedure. Results of experiment show that the frequency split of resonator is decreased to smaller than 0.03 Hz from original about 15 Hz.
TL;DR: The attitude measure system based on extended Kalman filter realized precision attitude measure for multi-rotors in dynamic circumstances and may lay foundation for UAV remote control.