MEMS Inertial Sensors Research Articles

Feasibility Study and Performance Analysis of a Gyroless Orientation Tracker

Inertial orientation tracking systems commonly use three types of sensors: accelerometers, magnetometers, and gyroscopes. The angular rate signal is used to obtain a dead reckoning estimate, whereas the gravitational and local magnetic field measures allow us to apply a correction and to obtain a drift-free result. Considering the present market of inertial MEMS sensors, the current consumption of gyroscopes represents a major part of the power budget of wireless inertial sensor nodes, which should be minimized given the mobility of the application. This paper introduces an orientation tracking algorithm, based on an unscented Kalman filter, that does not require angular rate data for tracking human movements up to 450 °/s , which is a reasonable value for many applications. Since accelerometers measure other accelerations beside gravity and magnetometers are prone to magnetic disturbances, adaptive techniques are applied in order to reduce the influence on the estimations. The performance of the system is quantitatively analyzed and compared to an estimator that includes angular rate information.

New Dynamics of the MEMS Inertial Sensor Market

We will present our new market forecast 2010-2016 for MEMS inertial sensors. The consumer and automotive inertial sensors market saw a healthy 15% growth in 2011 to surpass a $2.7 billion business, thanks to the increasing penetration of more motion sensing into more mobile devices and more automotive safety systems in more cars across the globe. The market and technology trends will be detailed. We will focus on the development of sensor combos. The key drivers to integrate several MEMS sensors in a package are to provide cost / size decrease, or to provide new functionalities with sensor fusion. We will see that sensor fusion with specific software brings a lot of the value, because MEMS players are now trying to sell functions rather than sensors. Lastly we will focus on describing the leading players and the hot developments in this area. The development of MEMS combos implies new business models which are shaking the MEMS industry. The complexity of the inertial MEMS supply chain and the major evolutions will be highlighted in this presentation.

Design and implementation of SMO for a nonlinear MIMO AHRS

In a low-cost attitude heading reference system (AHRS), the measurements made by MEMS inertial and magnetic sensors are affected by large parameter uncertainties, stochastic noises and unknown disturbances. In this paper, considering the robustness of the sliding mode observers (SMO) against both structured and unstructured uncertainties as well as exogenous inputs, the process of design and implementation of a nonlinear SMO is proposed for a low-cost AHRS. For simultaneous estimation of orientation variables and calibration biases of gyroscopes, a nonlinear and non-affine model of the AHRS is considered. Therefore, based on the Lie-algebraic method, the estimation algorithm is designed for a general class of non-affine nonlinear MIMO systems. In the proposed observer, owing to decreasing the required assumptions for coordinate transformation in recent literatures, the design process of the SMO is simplified. The gain matrices of the proposed SMO are obtained through ensuring the stability and the convergence of estimation errors based on Lyapunov's direct method. The expected tracking performance of the robust state and parameter estimation algorithm compared to that of the extended Kalman filter (EKF) is evaluated through simulations and real experiments of a strapped AHRS on a ground vehicle.

Real Time 3D Anaglyph Image Control Using MEMS Sensors

3-D Imaging refers to a technique for creating or enhancing the illusion of depth in an image by presenting two offset images separately to the left and right eye of the viewer. Anaglyph generation is one of the most cost effective methods of spectroscopic 3D imaging. Almost all human activities are related to some motion. In this paper we discuss about a method to control realtime anaglyph images based on the movement of MEMS inertial sensors. The aim is to track the movements of the sensor and accordingly build a system that controls anaglyph images simultaneously. A system to control anaglyph images using MEMS was developed at the end of experiment successfully. This can be further enhanced to develop various systems based on motion tracking and image creation, like human body modeling and gesture recognition and also to other 3D imaging techniques.

INS-Assisted GNSS Signal Tracking Modeling and Assessment

To improve the tracking performance of GNSS receiver in signal-attenuated environments, phase lock loop (PLL) and delay lock loop (DLL) assisted with Inertial Navigation System (INS) measurements are considered. Combining inertial navigation principles with signal processing, this paper proposes a simplified but efficient mathematical model of INS-assisted second-order tracking loops. Compared with unaided GNSS receiver, the tracking behavior of INS-assisted receiver is quantitatively analyzed, and the kind of INS suitable to guarantee the tracking condition is determined. The results indicate that an INS with 1 deg/h gyro drift is necessary to support PLL, and MEMS inertial sensor with 100 deg/h gyro drift is sufficient to aid DLL to keep favored tracking ability.

Two-Scale Simulation of Drop-Induced Failure of Polysilicon MEMS Sensors

In this paper, an industrially-oriented two-scale approach is provided to model the drop-induced brittle failure of polysilicon MEMS sensors. The two length-scales here investigated are the package (macroscopic) and the sensor (mesoscopic) ones. Issues related to the polysilicon morphology at the micro-scale are disregarded; an upscaled homogenized constitutive law, able to describe the brittle cracking of silicon, is instead adopted at the meso-scale. The two-scale approach is validated against full three-scale Monte-Carlo simulations, which allow for stochastic effects linked to the microstructural properties of polysilicon. Focusing on inertial MEMS sensors exposed to drops, it is shown that the offered approach matches well the experimentally observed failure mechanisms.

Vertical references for unmanned aerial vehicles

The paper considers vertical references, which determine UAV pitch and roll angles and are built as two-gyro pendulum-positioned systems, and strapdown attitude reference systems based on MEMS inertial sensors and pyrometers.

Gain-Scheduled Complementary Filter Design for a MEMS Based Attitude and Heading Reference System

This paper describes a robust and simple algorithm for an attitude and heading reference system (AHRS) based on low-cost MEMS inertial and magnetic sensors. The proposed approach relies on a gain-scheduled complementary filter, augmented by an acceleration-based switching architecture to yield robust performance, even when the vehicle is subject to strong accelerations. Experimental results are provided for a road captive test during which the vehicle dynamics are in high-acceleration mode and the performance of the proposed filter is evaluated against the output from a conventional linear complementary filter.

Attitude determination by integration of MEMS inertial sensors and GPS for autonomous agriculture applications

Integration of Global Positioning System (GPS) and Inertial Navigation System (INS) technologies, which has widespread usage in industry, is also regarded as an ideal solution for automated agriculture because it fulfils the accuracy, reliability and availability requirements of industrial and agricultural applications. Agriculture applications use position, velocity and heading information for automated vehicle guidance and control to enhance the yield and quality of the crop, and in order to vary the application of fertilizer and herbicides according to soil heterogeneity at sub-field level. A loosely coupled GPS/INS integration algorithm known as "AhrsKf" is introduced for automated agriculture vehicle guidance and control utilizing MEMS inertial sensors and GPS. The AhrsKf can produce high-frequency attitude solutions for the vehicle's guidance and control system, by using inputs from a single survey grade L1/L2 antenna, eliminating the need for the previous two antenna solutions. Given its agricultural application, the AhrsKf has been implemented with some specific design features to improve the accuracy of the attitude solution including, temperature compensation of the inertial sensors, and the aid of plough lines of farm lands. To evaluate the AhrsKf solution, two benchmarking tests have been conducted by using a three-antenna GPS system and NovAtel's SPAN-CPT. The results have demonstrated that the AhrsKf solution is stable and can correctly track the movement of the farming vehicle.

Comparison of Two Alternative Silicon-on-Glass Microfabrication Processes for MEMS Inertial Sensors

This paper presents experimental comparison of a modified silicon-on-glass (M-SOG) process to a previously-reported classical SOG (C-SOG) process based on the use of SOI wafers, yielding a stress free <111> silicon structural layer with desired structure thickness. The basic difference between these processes is the sequence of the step at which the silicon microstructures are defined by DRIE, making M-SOG more robust against critical dimension (CD) variations. Overall, M-SOG provides a simple, high yield, reliable, and robust solution for producing high performance MEMS inertial sensors, and these advantages are experimentally verified over C-SOG, both completed by following the identical process parameters and by using the same mask set.

MP-54 MEMS慣性センサのクロストーク低減を目的としたマイクロばね構造の開発と評価 : 高速度カメラによるばね運動の直接観察(ポスターセッション)

MP-54 MEMS慣性センサのクロストーク低減を目的としたマイクロばね構造の開発と評価 : 高速度カメラによるばね運動の直接観察(ポスターセッション)

静電容量検出型集積化MEMS慣性センサの固有振動数設計

静電容量検出型集積化MEMS慣性センサの固有振動数設計

A NEW CALIBRATION METHOD FOR LOW COST MEMS INERTIAL SENSOR MODULE

This paper presents a new calibration method to overcome the challenges of MEMS inertial sensors for underwater navigation. The MEMS inertial sensor module is composed of an accelerometer, a gyroscope and a circuit of signal process. For navigation estimation, it is easy to be influenced by errors which come from MEMS inertial sensors. In general, the sources of error can be categorized into two groups, deterministic type and stochastic type. The former primarily includes bias errors, misalignment and nonlinearity; the latter contains temperature effect and signal drifting. Subsequently, the linearity calibration is used to modify the deterministic error and the wavelet analysis can suppress the stochastic noise. Therefore, the new calibration method integrated of linearity calibration and wavelet signal processing is utilized to enhance the accuracy and performance of MEMS inertial sensor module. The experimental results demonstrate that the output signal can be corrected suitability by means of the proposed method.

Enhanced MEMS-IMU/odometer/GPS integration using mixture particle filter

Dead reckoning techniques such as inertial navigation and odometry are integrated with GPS to avoid interruption of navigation solutions due to lack of visible satellites. A common method to achieve a low-cost navigation solution for land vehicles is to use a MEMS-based inertial measurement unit (IMU) for integration with GPS. This integration is traditionally accomplished by means of a Kalman filter (KF). Due to the significant inherent errors of MEMS inertial sensors and their time-varying changes, which are difficult to model, severe position error growth happens during GPS outages. The positional accuracy provided by the KF is limited by its linearized models. A Particle filter (PF), being a nonlinear technique, can accommodate for arbitrary inertial sensor characteristics and motion dynamics. An enhanced version of the PF, called Mixture PF, is employed in this paper. It samples from both the prior importance density and the observation likelihood, leading to an improved performance. Furthermore, in order to enhance the performance of MEMS-based IMU/GPS integration during GPS outages, the use of pitch and roll calculated from the longitudinal and transversal accelerometers together with the odometer data as a measurement update is proposed in this paper. These updates aid the IMU and limit the positional error growth caused by two horizontal gyroscopes, which are a major source of error during GPS outages. The performance of the proposed method is examined on road trajectories, and results are compared to the three different KF-based solutions. The proposed Mixture PF with velocity, pitch, and roll updates outperformed all the other solutions and exhibited an average improvement of approximately 64% over KF with the same updates, about 85% over KF with velocity updates only, and around 95% over KF without any updates during GPS outages.

Distance Estimation using Intelligent Fusion of Navigation Data

INS/GPS navigation has changed due to advances in inertial manufacturing technology. This has created sensors (accelerometers, gyroscopes, magnetometers) that can be packaged with a GPS receiver/antenna for everyday use. These new manufacturing technologies are creating inertial sensors such as MEMS inertial sensors that are smaller and consume less power, but they also exhibit much larger errors comparing with their higher priced units. Kalman filtering is the main technique for combined IMU/GPS sensor data processing. Intelligent algorithm application for sensor data fusing is one of the newest areas of research. In the current paper the simplified intelligent algorithm is developed for GPS and accelerometer data combined processing. The performance of developed algorithm was checked through made experiments for estimation of distance passed by vehicle. The compare of passed distance estimation error for standard and intelligent Kalman algorithm are presented in the paper. Presented intelligent Kalman algorithm has smaller estimation error, simple in use and not demanding for computing power.

Wellbore Surveying While Drilling Based on Kalman Filtering

Problem statement: Oil and gas are global fuels obtained primarily from drilling wells in underground terrestrial reservoirs. Vertical drilling is preferred because of its simplicity and therefore low cost, but subsurface targets can often be procured only by directing the wellbore along predefined non-vertical trajectories. For instance, directional drilling must be employed to reach locations inaccessible to the drilling rig, to side track an existing well (multilateral drilling), or to drill multiple wells from the same offshore platform (horizontal drilling). Approach: A complete knowledge of the wellbore direction and orientation during the drilling process is essential to guarantee proper directional drilling procedure. Results: Thus, besides the conventional drilling assembly, directional drilling operations require sensors to provide azimuth, inclination and toolface angles of the drill. These sensors are part of the Measurement-While-Drilling (MWD) tool, which in current technology is installed several feet behind the drill bit. In such systems, values for inclination and toolface angles are determined from accelerometer measurements at predetermined stationary surveying stations; these values are then incorporated with magnetometer measurements to deliver the azimuth angle. Values for inclination and azimuth angles at the current surveying station are combined with those from the previous station to compute the position of the probe. However, there is no accurate information about the wellbore trajectory between survey stations. Additionally, the magnetic field of the magnetometers has deleterious effect on the overall accuracy of surveying measurements. Conclusion: A method to provide continuous information about the wellbore trajectory has been developed in this study. The module developed integrates a Rotary Steerable System (RSS) and MWD tool into one drilling probe utilizing Inertial Navigation System (INS) technology. This is achieved by designing a reliable real-time low cost MWD surveying system based on MEMS inertial sensors miniaturized inside the RSS housing installed directly behind the drill bit. A continuous borehole surveying module based on MEMS inertial sensors integrated with other drilling measurements was developed using Kalman filtering.

Low-Cost Three-Dimensional Navigation Solution for RISS/GPS Integration Using Mixture Particle Filter

Recent technological advances in both GPS and low-cost microelectromechanical-system (MEMS)-based inertial sensors have enabled the monitoring of the location of moving platforms for numerous positioning and navigation (POS/NAV) applications. GPS is presently widely used in land vehicles. However, in some environments, the GPS signal may suffer from signal blockage and multipath effects that deteriorate the positioning accuracy. When miniaturized inside any moving platforms, the MEMS-based inertial navigation system (INS) can be integrated with GPS and enhance the performance in denied GPS environments (like in urban canyons). Targeting a low-cost navigation solution for land vehicles, this paper uses a reduced inertial sensor system (RISS) with MEMS-based inertial sensors. In this paper, the RISS consists of one single-axis gyroscope and a two-axis accelerometer used together with the vehicle's odometer, and the whole system is integrated with GPS to obtain a 3-D navigation solution. The traditional technique for this integration problem is Kalman filtering (KF). Due to the inherent errors of MEMS inertial sensors and the relatively high noise levels associated with their measurements, KF has limited capabilities in providing accurate positioning. Particle filtering (PF) was recently suggested as a nonlinear filtering technique to accommodate arbitrary inertial sensor characteristics, motion dynamics, and noise distributions. An enhanced version of PF is utilized in this paper and is called Mixture PF. The performance of the proposed 3-D navigation solution using Mixture PF for RISS/GPS integration is examined by road-test trajectories in a land vehicle. The proposed method is compared with four other solutions: 1) 3-D solution using KF for full INS/GPS integration; 2) 2-D solution using KF for RISS/GPS integration; 3) 2-D solution using Mixture PF for RISS/GPS integration; and 4) 3-D solution using sampling/importance resampling (SIR) PF for RISS/GPS integration. The experimental results show that the proposed solution outperforms all the compared counterparts.

Determining a free flight performance surface by mathematical optimization techniques utilizing an air speed indicator, MEMS inertial sensors and a variomete

Paragliding is unpowered flight in which pilots rely on their ability to navigate rising currents of air to remain airborne. Paraglider flight performance is an important measure of the capabilities of a particular design of a canopy. Most often, the performance characteristics of a canopy are measured as horizontal velocity vs. vertical velocity for steady state flight in still air. The performance curve created using this approach neglects to take into account the effect which turning has on flight. In contrast, the performance surface created from the research carried out in this paper demonstrates the effect of turning on canopy flight; such a representation of performance is novel to the authors' knowledge. To produce this surface, a flight was conducted in which a paraglider's performance was measured for various steady state velocities and turning rates; the data were then analyzed utilizing mathematical optimization after appropriate calibration corrections were applied.

Upgrade of a computer-writing tool with MEMS inertial sensors

This paper presents an improvement to gesture reconstruction with a computer-writing tool based on an inertial principle. The computer-writing tool is modified with a complete, miniaturized, six degrees-of-freedom inertial measurement unit in order to reduce the error in the tool’s orientation with respect to the reference coordinate system and, consequently, to reduce the error in the tool’s tip position on the paper. The improvement is achieved by calibrating the inertial measurement unit using advanced modeling of the nonlinear voltage output of a triple-axis low-g MEMS accelerometer. The nonlinear accelerometer’s model considers an interacting influence between the measuring axes of the arbitrarily oriented writing tool. With a combination of the attitude compensation and the zero-velocity compensation techniques the positional error of the tool’s tip on the paper is reduced.

Fault detection and isolation enhancement of an aircraft attitude and heading reference system based on MEMS inertial sensors

The process of enriching a multisensor avionic strap-down attitude estimation unit with fault detection and isolation (FDI) capabilities is presented. Compact solid-state sensors are integrated in an autonomous emergency guidance system for aircrafts. Optimal attitude estimate is provided by a 9-state extended Kalman filter for fusion of complementary inertial data. Observerbased residual generation can be consequently added with negligible computational overhead. On-line FDI of the different sensors is analyzed and achieved with minimal hardware and software modifications. The proposed detection scheme combines different approaches: physical redundancy (accelerometers), injection of spectrally-decoupled test signals (magnetic sensors) and analytical redundancy (gyros).