Sangkyung Sung

On the Mode-Matched Control of MEMS Vibratory Gyroscope via Phase-Domain Analysis and Design

This paper investigates a novel method for the mode-matched control of a microelectromechanical systems (MEMS) vibratory gyroscope through a phase-domain analysis. Compared with the previous works, the proposed method presents a simple and robust automatic mode tuning scheme for sensitivity enhancement. In designing the mode-matched control loop, the resonant characteristics of the driving axis are used as the reference mode. Then, the phase difference between sense and drive modes at the resonant frequency of drive mode is used to generate a control signal for phase error regulation. For the control loop design, a linear phase-locked loop is adapted. Through the simulation using practical MEMS gyroscope parameters, the mode-matching performance and robustness of the designed control loop is demonstrated. It is also shown that coupling effect yields no degradation of output sensitivity. Finally, the experimental results obtained by implementing the electronics of mode-matched control verify the feasibility of the proposed method.

Design and performance test of a MEMS vibratory gyroscope with a novel AGC force rebalance control

In this paper, the development and performance test results of a laterally oscillating MEMS gyroscope using a novel force rebalance control strategy are presented. The micromachined structure and electrodes are fabricated using the deep reactive ion etching (DRIE) and anodic wafer bonding process. The high quality factor required for the resonance-based sensor is achieved using a vacuum-sealed device package. A systematic design approach of the force rebalance control is applied via a modified automatic gain control (AGC) method. The rebalance control design takes advantages of a novel AGC loop modification, which allows the approximation of the systems dynamics into a simple linear form. Using the proposed modification of AGC and the rebalance strategy that maintains a biased oscillation, a number of performance improvements including bandwidth extension and widened operating range were observed to be achieved. Finally, the experimental results of the gyroscopes practical application verify the feasibility and performance of the developed sensor.

Development of a lateral velocity-controlled MEMS vibratory gyroscope and its performance test

The objective of this paper is to present a velocity-controlled vibratory MEMS gyroscope that achieves consistent output characteristics in the lateral driving dynamics of the system. Through a systematic automatic gain control loop design process, the driving mode dynamics of the gyroscope is first transformed to take account of the velocity envelope; a reference tracking integral control is then employed. For stabilized loop construction, a mathematical development and stability analysis of the feedback loop is presented, which is followed by numerical simulation using practical sensor parameters. The mechanical structure was fabricated using the conventional deep reactive ion etching process and the anodic wafer bonding method. Vacuum-packaged devices were used for the resonant gyroscope operation. An essential fabrication process for realizing the electrical connection through a thick glass substrate was possible by applying a sandblasting process and spin coating process of conductive epoxy. Finally, loop simulation and experimental results verified that the amplitude-controlled property in the driving loop is preserved under the system parameter variation which resulted in enhanced gyroscope output performance in comparison with other driving schemes.

Error Calibration of Magnetometer Using Nonlinear Integrated Filter Model With Inertial Sensors

This paper presents an onboard heading estimation algorithm using IMU and strapdown magnetometer without other external heading references. To calibrate the magnetic deviation, sensor errors caused by hard iron effect and initial heading of strapdown magnetometers are considered. In our approach, sensor output distortion due to the soft iron effect is ignored, which is relatively small. First, for the estimation of heading angle, system and measurement model is presented. Then particle filter and extended Kalman filter is introduced for performance comparison. The proposed algorithm for the integration of IMU and magnetometer is verified via numerical simulation using Matlab. Simulation result demonstrates accurate heading estimation error under 1 degree for both algorithms when there exists a small initial heading error and hard iron effect, yet particle filter provides more robust and accurate result than the extended Kalman filter in case the initial heading error and biases are large.

GNSS integration with vision-based navigation for low GNSS visibility conditions

In urban canyons, buildings and other structures often block the line of sight of visible Global Navigation Satellite System (GNSS) satellites, which makes it difficult to obtain four or more satellites to provide a three-dimensional navigation solution. Previous studies on this operational environment have been conducted based on the assumption that GNSS is not available. However, a limited number of satellites can be used with other sensor measurements, although the number is insufficient to derive a navigation solution. The limited number of GNSS measurements can be integrated with vision-based navigation to correct navigation errors. We propose an integrated navigation system that improves the performance of vision-based navigation by integrating the limited GNSS measurements. An integrated model was designed to apply the GNSS range and range rate to vision-based navigation. The possibility of improved navigation performance was evaluated during an observability analysis based on available satellites. According to the observability analysis, each additional satellite decreased the number of unobservable states by one, while vision-based navigation always has three unobservable states. A computer simulation was conducted to verify the improvement in the navigation performance by analyzing the estimated position, which depended on the number of available satellites; additionally, an experimental test was conducted. The results showed that limited GNSS measurements can improve the positioning performance. Thus, our proposed method is expected to improve the positioning performance in urban canyons.

Weighted DOP With Consideration on Elevation-Dependent Range Errors of GNSS Satellites

This paper proposes the weighted dilution of precision (WDOP) with consideration of the satellite elevation angle in order to improve the performance of dilution of precision (DOP), which is a standard tool to quantify the positional precision of the Global Navigation Satellite System (GNSS). The WDOP is calculated by assigning different weights to visible GNSS satellites depending on their elevation angles. In order to demonstrate the effectiveness of WDOP, the conventional DOP and WDOP were mathematically analyzed and a comparative analysis was conducted using actual Global Positioning System data. Results showed that WDOP represents the position error trends more accurate than the conventional DOP, particularly when low-elevation measurements were used for positioning calculation. Therefore, the WDOP could be a promising replacement of DOP as a tool for representing and quantifying errors in GNSS positioning.

Improving mobile robot navigation performance using vision based SLAM and distributed filters

In this paper, we suggest a vision-based SLAM (simultaneous localization and mapping) method to improve navigation performance of mobile robot, which is used 2 encoders to calculate its position. If mobile robot is in building, tunnel or under ground facility, it is difficult to obtain navigation information from GPS only navigation system, because there are not enough visible GPS satellites. To overcome this limitation, DR (dead reckoning) system is required. However, as DR operation time goes by, the navigation error is increased because of accumulation of sensor error and noise. There are variety kinds of methods to reduce these errors. In this paper, we use a vision sensor and particle filter. Some clear points on vision sensor image are selected and tracked for error compensation. That is called a SLAM (simultaneous localization and mapping) method. In this paper, distributed particle filter is used to cope with nonlinear observation model and to deal with changing the number of measurements. Computer simulations are conducted to demonstrate the performance of suggested filter.

An efficient GPS parameter prediction method using GPS ephemeris patterns for Self-Assisted GPS

Ephemeris parameters in navigation message data which are updated every 2 hours has irregular patterns, But in this works, we discovered regular patterns in 24h interval some ephemeris parameters (Semi-Major Axis, Eccentricity, Mean Motion, Inclination Angle, Rate of Inclination Angle, Harmonic Perturbation Terms). In this paper, we evaluate the ephemeris parameter estimation algorithms for SA-GPS using 24h interval GPS ephemeris patterns. Using 24 hours ephemeris parameter regular patterns, we propose simple estimation methods (Polynomial method, Sine estimation method) for estimation of current ephemeris parameters.

Orbit Ephemeris Failure Detection in a GNSS Regional Application

To satisfy civil aviation requirements using the Global Navigation Satellite System (GNSS), it is important to guarantee system integrity. In this work, we propose a fault detection algorithm for GNSS ephemeris anomalies. The basic principle concerns baseline length estimation with GNSS measurements (pseudorange, broadcasted ephemerides). The estimated baseline length is subtracted from the true baseline length, computed using the exact surveyed ground antenna positions. If this subtracted value differs by more than a given threshold, this indicates that an ephemeris anomaly has been detected. This algorithm is suitable for detecting Type A ephemeris failure, and more advantageous for use with multiple stations with various long baseline vectors. The principles of the algorithm, sensitivity analysis, minimum detectable error (MDE), and protection level derivation are described and we verify the sensitivity analysis and algorithm availability based on real GPS data in Korea. Consequently, this algorithm is appropriate for GNSS regional implementation.

Integrated Navigation Design Using a Gimbaled Vision/LiDAR System with an Approximate Ground Description Model

This paper presents a vision/LiDAR integrated navigation system that provides accurate relative navigation performance on a general ground surface, in GNSS-denied environments. The considered ground surface during flight is approximated as a piecewise continuous model, with flat and slope surface profiles. In its implementation, the presented system consists of a strapdown IMU, and an aided sensor block, consisting of a vision sensor and a LiDAR on a stabilized gimbal platform. Thus, two-dimensional optical flow vectors from the vision sensor, and range information from LiDAR to ground are used to overcome the performance limit of the tactical grade inertial navigation solution without GNSS signal. In filter realization, the INS error model is employed, with measurement vectors containing two-dimensional velocity errors, and one differenced altitude in the navigation frame. In computing the altitude difference, the ground slope angle is estimated in a novel way, through two bisectional LiDAR signals, with a practical assumption representing a general ground profile. Finally, the overall integrated system is implemented, based on the extended Kalman filter framework, and the performance is demonstrated through a simulation study, with an aircraft flight trajectory scenario.