Characterization of errors and noises in MEMS inertial sensors using Allan variance method

This thesis work has addressed the problems of characterizing and identifying the noises inherent to inertial sensors as gyros and accelerometers, which are embedded in inertial navigation systems, with the purpose of estimating the errors on the obtained position. The analysis of the Allan Variance method (AVAR) to characterize and identify the noises related to these sensors, has been done. The practical implementation of the AVAR method for the noises characterization has been performed over an experimental setup using the IMU 3DM-GX3 -25 data and the Matlab environment. From the AVAR plots it was possible to identify the Angle Random Walk and the Bias Instability in the gyros, and the Velocity Random Walk and Bias Instability in the accelerometers. A denoising process was also performed by using the Discrete Wavelet Transforms and the Median Filter. After the filtering the AVAR plots showed that the ARW was almost removed or attenuated using Wavelets, but not good results were obtained with the Median Filter

Reliability Testing Procedure for MEMS IMUs Applied to Vibrating Environments

The diffusion of micro electro-mechanical systems (MEMS) technology applied to navigation systems is rapidly increasing, but currently, there is a lack of knowledge about the reliability of this typology of devices, representing a serious limitation to their use in aerospace vehicles and other fields with medium and high requirements. In this paper, a reliability testing procedure for inertial sensors and inertial measurement units (IMU) based on MEMS for applications in vibrating environments is presented. The sensing performances were evaluated in terms of signal accuracy, systematic errors, and accidental errors; the actual working conditions were simulated by means of an accelerated dynamic excitation. A commercial MEMS-based IMU was analyzed to validate the proposed procedure. The main weaknesses of the system have been localized by providing important information about the relationship between the reliability levels of the system and individual components

Degree-per-hour mode-matched micromachined silicon vibratory gyroscopes

The objective of this research dissertation is to design and implement two novel micromachined silicon vibratory gyroscopes, which attempt to incorporate all the necessary attributes of sub-deg/hr noise performance requirements in a single framework: large resonant mass, high drive-mode oscillation amplitudes, large device capacitance (coupled with optimized electronics), and high-Q resonant mode-matched operation. Mode-matching leverages the high-Q (mechanical gain) of the operating modes of the gyroscope and offers significant improvements in mechanical and electronic noise floor, sensitivity, and bias stability. The first micromachined silicon vibratory gyroscope presented in this work is the resonating star gyroscope (RSG): a novel Class-II shell-type structure which utilizes degenerate flexural modes. After an iterative cycle of design optimization, an RSG prototype was implemented using a multiple-shell approach on (111) SOI substrate. Experimental data indicates sub-5 deg/hr Allan deviation bias instability operating under a mode-matched operating Q of 30,000 at 23ºC (in vacuum). The second micromachined silicon vibratory gyroscope presented in this work is the mode-matched tuning fork gyroscope (M2-TFG): a novel Class-I tuning fork structure which utilizes in-plane non-degenerate resonant flexural modes. Operated under vacuum, the M2-TFG represents the first reported high-Q perfectly mode-matched operation in Class-I vibratory microgyroscope. Experimental results of device implemented on (100) SOI substrate demonstrates sub-deg/hr Allan deviation bias instability operating under a mode-matched operating Q of 50,000 at 23ºC. In an effort to increase capacitive aspect ratio, a new fabrication technology was developed that involved the selective deposition of doped-polysilicon inside the capacitive sensing gaps (SPD Process). By preserving the structural composition integrity of the flexural springs, it is possible to accurately predict the operating-mode frequencies while maintaining high-Q operation. Preliminary characterization of vacuum-packaged prototypes was performed. Initial results demonstrated high-Q mode-matched operation, excellent thermal stability, and sub-deg/hr Allan variance bias instability.Ph.D.Committee Chair: Dr. Farrokh Ayazi; Committee Member: Dr. Mark G. Allen; Committee Member: Dr. Oliver Brand; Committee Member: Dr. Paul A. Kohl; Committee Member: Dr. Thomas E. Michael

Inertial measurement unit modelling

Inerciální měřící jednotka (IMU) patří mezi základní senzorické vybavení současných mobilních robotů, kde se používá především pro odhadování ohamžité orientace robotu v prostoru. V této práci se komplexně zabývám simulací IMU v robotickém simulátoru Gazebo za účelem co nejvěrnějšího modelování odhadovaného úhlu natočení robotu, který je jedním z přímých výstupů IMU senzoru Bosch BNO055. Pro podporu vyhodnocení kvality IMU modelu vzhledem k reálným datům z BNO055 jsem navrhl a implementoval simulační prostřední v rámci Robotického Operačního Systému (ROS), které aproximuje zadanou trajektorii, uloží data ze simulovaného IMU a vygeneruje podklady pro vyhodnocení kvality IMU modelu vzhledem k reálným datům z BNO055. Na základě nejlepších dostupných IMU pluginů v Gazebu jsem implementoval dva URDF/SDF modely IMU sensoru, jejichž funkčnost byla následně ověřena řadou experimentů v simulátoru. Provedené simulace potvrdily funkčnost modelů a zároveň poukázaly na limity realističnosti současných pluginů v Gazebu a nastínily možnosti dalšího vývoje pro zvýšení věrnosti simulací IMU.The inertial measurement unit (IMU) sensors are massively used in mobile service robots to provide orientation estimation. This thesis is concerned with modeling and simulation of IMU sensor in the robotics simulator Gazebo. The main goal of this thesis is to simulate the heading angle output of a real IMU sensor Bosch BNO055 with high fidelity. To enable the IMU model evaluation I designed and implmented a custom IMU simulation framework as a ROS package. This framework approximates the given trajectory with the help of a Gazebo simulation of a robot model with attached IMU sensor model, captures the simulated IMU output and generates data for the comparison concerning provided dataset measured by real BNO055. I used the best currently available IMU plugins to implement two different URDF/SDF IMU models. The simulations demonstrated the functionality of implemented IMU models, but also revealed the fidelity limitations of current IMU plugins in Gazebo, and led to a discussion about possible future improvements

Validation and Experimental Testing of Observers for Robust GNSS-Aided Inertial Navigation

This chapter is the study of state estimators for robust navigation. Navigation of vehicles is a vast field with multiple decades of research. The main aim is to estimate position, linear velocity, and attitude (PVA) under all dynamics, motions, and conditions via data fusion. The state estimation problem will be considered from two different perspectives using the same kinematic model. First, the extended Kalman filter (EKF) will be reviewed, as an example of a stochastic approach; second, a recent nonlinear observer will be considered as a deterministic case. A comparative study of strapdown inertial navigation methods for estimating PVA of aerial vehicles fusing inertial sensors with global navigation satellite system (GNSS)-based positioning will be presented. The focus will be on the loosely coupled integration methods and performance analysis to compare these methods in terms of their stability, robustness to vibrations, and disturbances in measurements

Development and Validation of an IMU/GPS/Galileo Integration Navigation System for UAV

Several and distinct Unmanned Aircraft Vehicle (UAV) applications are emerging, demanding steps to be taken in order to allow those platforms to operate in an un-segregated airspace. The key risk component, hindering the widespread integration of UAV in an un-segregated airspace, is the autonomous component: the need for a high level of autonomy in the UAV that guarantees a safe and secure integration in an un-segregated airspace. At this point, the UAV accurate state estimation plays a fundamental role for autonomous UAV, being one of the main responsibilities of the onboard autopilot. Given the 21st century global economic paradigm, academic projects based on inexpensive UAV platforms but on expensive commercial autopilots start to become a non-economic solution. Consequently, there is a pressing need to overcome this problem through, on one hand, the development of navigation systems using the high availability of low cost, low power consumption, and small size navigation sensors offered in the market, and, on the other hand, using Global Navigation Satellite Systems Software Receivers (GNSS SR). Since the performance that is required for several applications in order to allow UAV to fly in an un-segregated airspace is not yet defined, for most UAV academic applications, the navigation system accuracy required should be at least the same as the one provided by the available commercial autopilots. This research focuses on the investigation of the performance of an integrated navigation system composed by a low performance inertial measurement unit (IMU) and a GNSS SR. A strapdown mechanization algorithm, to transform raw inertial data into navigation solution, was developed, implemented and evaluated. To fuse the data provided by the strapdown algorithm with the one provided by the GNSS SR, an Extended Kalman Filter (EKF) was implemented in loose coupled closed-loop architecture, and then evaluated. Moreover, in order to improve the performance of the IMU raw data, the Allan variance and denoise techniques were considered for both studying the IMU error model and improving inertial sensors raw measurements. In order to carry out the study, a starting question was made and then, based on it, eight questions were derived. These eight secondary questions led to five hypotheses, which have been successfully tested along the thesis. This research provides a deliverable to the Project of Research and Technologies on Unmanned Air Vehicles (PITVANT) Group, consisting of a well-documented UAV Development and Validation of an IMU/GPS/Galileo Integration Navigation System for UAV II navigation algorithm, an implemented and evaluated navigation algorithm in the MatLab environment, and Allan variance and denoising algorithms to improve inertial raw data, enabling its full implementation in the existent Portuguese Air Force Academy (PAFA) UAV. The derivable provided by this thesis is the answer to the main research question, in such a way that it implements a step by step procedure on how the Strapdown IMU (SIMU)/GNSS SR should be developed and implemented in order to replace the commercial autopilot. The developed integrated SIMU/GNSS SR solution evaluated, in post-processing mode, through van-test scenario, using real data signals, at the Galileo Test and Development Environment (GATE) test area in Berchtesgaden, Germany, when confronted with the solution provided by the commercial autopilot, proved to be of better quality. Although no centimetre-level of accuracy was obtained for the position and velocity, the results confirm that the integration strategy outperforms the Piccolo system performance, being this the ultimate goal of this research work

On Improving the Accuracy and Reliability of GPS/INS-Based Direct Sensor Georeferencing

Due to the complementary error characteristics of the Global Positioning System (GPS) and Inertial Navigation System (INS), their integration has become a core positioning component, providing high-accuracy direct sensor georeferencing for multi-sensor mobile mapping systems. Despite significant progress over the last decade, there is still a room for improvements of the georeferencing performance using specialized algorithmic approaches. The techniques considered in this dissertation include: (1) improved single-epoch GPS positioning method supporting network mode, as compared to the traditional real-time kinematic techniques using on-the-fly ambiguity resolution in a single-baseline mode; (2) customized random error modeling of inertial sensors; (3) wavelet-based signal denoising, specially for low-accuracy high-noise Micro-Electro-Mechanical Systems (MEMS) inertial sensors; (4) nonlinear filters, namely the Unscented Kalman Filter (UKF) and the Particle Filter (PF), proposed as alternatives to the commonly used traditional Extended Kalman Filter (EKF). The network-based single-epoch positioning technique offers a better way to calibrate the inertial sensor, and then to achieve a fast, reliable and accurate navigation solution. Such an implementation provides a centimeter-level positioning accuracy independently on the baseline length. The advanced sensor error identification using the Allan Variance and Power Spectral Density (PSD) methods, combined with a wavelet-based signal de-noising technique, assures reliable and better description of the error characteristics, customized for each inertial sensor. These, in turn, lead to a more reliable and consistent position and orientation accuracy, even for the low-cost inertial sensors. With the aid of the wavelet de-noising technique and the customized error model, around 30 percent positioning accuracy improvement can be found, as compared to the solution using raw inertial measurements with the default manufacturer’s error models. The alternative filters, UKF and PF, provide more advanced data fusion techniques and allow the tolerance of larger initial alignment errors. They handle the unknown nonlinear dynamics better, in comparison to EKF, resulting in a more reliable and accurate integrated system. For the high-end inertial sensors, they provide only a slightly better performance in terms of the tolerance to the losses of GPS lock and orientation convergence speed, whereas the performance improvements are more pronounced for the low-cost inertial sensors

Interface Circuit for a Multiple-Beam Tuning-Fork Gyroscope with High Quality Factors

This research work presents the design, theoretical analysis, fabrication, interface electronics, and experimental results of a Silicon-On-Insulator (SOI) based Multiple-Beam Tuning-Fork Gyroscope (MB-TFG). Based on a numerical model of Thermo-Elastic Damping (TED), a Multiple-Beam Tuning-Fork Structure (MB-TFS) is designed with high Quality factors (Qs) in its two operation modes. A comprehensive theoretical analysis of the MB-TFG design is conducted to relate the design parameters to its operation parameters and further performance parameters. In conjunction with a mask that defines the device through trenches to alleviate severe fabrication effect on anchor loss, a simple one-mask fabrication process is employed to implement this MB-TFG design on SOI wafers. The fabricated MB-TFGs are tested with PCB-level interface electronics and a thorough comparison between the experimental results and a theoretical analysis is conducted to verify the MB-TFG design and accurately interpret the measured performance. The highest measured Qs of the fabricated MB-TFGs in vacuum are 255,000 in the drive-mode and 103,000 in the sense-mode, at a frequency of 15.7kHz. Under a frequency difference of 4Hz between the two modes (operation frequency is 16.8kHz) and a drive-mode vibration amplitude of 3.0μm, the measured rate sensitivity is 80μVpp/°/s with an equivalent impedance of 6MΩ. The calculated overall rate resolution of this device is 0.37/°hr/√Hz, while the measured Angle Random Walk (ARW) and bias instability are 6.67°/\u27√hr and 95°/hr, respectively

CMOS systems and circuits for sub-degree per hour MEMS gyroscopes

The objective of our research is to develop system architectures and CMOS circuits that interface with high-Q silicon microgyroscopes to implement navigation-grade angular rate sensors. The MEMS sensor used in this work is an in-plane bulk-micromachined mode-matched tuning fork gyroscope (M² – TFG ), fabricated on silicon-on-insulator substrate. The use of CMOS transimpedance amplifiers (TIA) as front-ends in high-Q MEMS resonant sensors is explored. A T-network TIA is proposed as the front-end for resonant capacitive detection. The T-TIA provides on-chip transimpedance gains of 25MΩ, has a measured capacitive resolution of 0.02aF /√Hz at 15kHz, a dynamic range of 104dB in a bandwidth of 10Hz and consumes 400μW of power. A second contribution is the development of an automated scheme to adaptively bias the mechanical structure, such that the sensor is operated in the mode-matched condition. Mode-matching leverages the inherently high quality factors of the microgyroscope, resulting in significant improvement in the Brownian noise floor, electronic noise, sensitivity and bias drift of the microsensor. We developed a novel architecture that utilizes the often ignored residual quadrature error in a gyroscope to achieve and maintain perfect mode-matching (i.e.0Hz split between the drive and sense mode frequencies), as well as electronically control the sensor bandwidth. A CMOS implementation is developed that allows mode-matching of the drive and sense frequencies of a gyroscope at a fraction of the time taken by current state of-the-art techniques. Further, this mode-matching technique allows for maintaining a controlled separation between the drive and sense resonant frequencies, providing a means of increasing sensor bandwidth and dynamic range. The mode-matching CMOS IC, implemented in a 0.5μm 2P3M process, and control algorithm have been interfaced with a 60μm thick M2−TFG to implement an angular rate sensor with bias drift as low as 0.1°/hr ℃ the lowest recorded to date for a silicon MEMS gyro.Ph.D.Committee Chair: Farrokh Ayazi; Committee Member: Jennifer Michaels; Committee Member: Levent Degertekin; Committee Member: Paul Hasler; Committee Member: W. Marshall Leac