Microelectromechanical Systems Inertial Sensors Research Articles

(Invited) A Sub-1G MEMS Sensor

MEMS (microelectromechanical systems) inertial sensors, such as MEMS accelerometers and gyroscopes, have now been in widespread use as a variety of applications such as automotive, industrial, healthcare, and consumer use and so forth. The driving force behind the emergence of the inertial sensors is the development of silicon MEMS technology [1] that has enabled us to miniaturize all the mechanical components down to micro-meter scale. In addition, by taking the advantage of semiconductor manufacturing process, low production cost and high functionalities has also been achieved, which succeeded in commercializing MEMS inertial sensors. In this talk, we show our recent research progress of highly-sensitive MEMS accelerometer for monitoring micro motion at the level of sub-1G (1 G = 9.8 m/s2) acceleration for CMOS-MEMS accelerometer. From the view point of inertial force, commercially available sensors deals mostly with more than 1 G. On the other hand, demands are escalating for the accurate sensing of sub-1G acceleration for the precise monitoring of human and non-human objects. The major challenge for sensing sub-1G region is to reduce the thermo-mechanical noise [2], as it is called Brownian noise (BN). BN dominates the sensor noise that degrades the sensing resolution, and the magnitude of BN is inversely proportional to the proof mass;increasing the density of the proof mass is an effective way to suppress the BN without compromising the device footprint. According to the above background, we have utilized gold as a material of the proof mass. As shown in Fig. 1, the density of gold is nearly an order of magnitude higher than that of silicon used as a standard material of proof mass, and therefore the BN can be small enough to detect below 1 G with high resolution even when the sensor size is as small as silicon MEMS accelerometers.Along with the material consideration, we have also developed the fabrication process for single chip integration with the MEMS structures and the sensing circuitry, as schematically described in Fig. 2; this solution is beneficial not only to eliminate the parasitic effects but also to efficiently save the chip footprint in contrast to multi-chip integration or other CMOS-MEMS approaches that separate the chip area depending on the functionalities. Toward the proposed sensor design, we have employed the post-CMOS process [3] using gold electroplating, which can implement the sensor onto CMOS chip. Efforts are being made for the electroplated gold to increase the thickness for large proof mass and to laminate multiple layers to enhance the design flexibility.Proof-of-concept devices have been produced on silicon substrate, as the one of them is seen in Fig. 3(a) and (b), by using the aforementioned microfabrication techniques, and the BN was estimated to be less than 1 µG/Hz1/2[4]. By stacking multiple layers, all the mechanical components, such as the mechanical springs and the stoppers have been designed by optimizing each layer thickness. The capacitance shift associated with input acceleration below 1 G was also measured on the device to evaluate the potential of capacitive accelerometer. The presentation will cover the design, fabrication process, and measurement results of the MEMS devices for sub-1G sensing.[1] K. E. Petersen, Proc. IEEE, 70, p. 420, 1982. [2] B. E. Boser and R. T. Howe, IEEE J. Solid-State Circuits, 31, p. 366, 1996. [3] T. Konishi et al., Jpn. J. Appl. Phys., 52, p. 06GL04, 2013. [4] D. Yamane et al., Appl. Phys. Lett., 104, p. 074102, 2014. Figure 1

Invariant Observer-Based State Estimation for Micro-Aerial Vehicles in GPS-Denied Indoor Environments Using an RGB-D Camera and MEMS Inertial Sensors

This paper presents a non-linear state observer-based integrated navigation scheme for estimating the attitude, position and velocity of micro aerial vehicles (MAV) operating in GPS-denied indoor environments, using the measurements from low-cost MEMS (micro electro-mechanical systems) inertial sensors and an RGB-D camera. A robust RGB-D visual odometry (VO) approach was developed to estimate the MAV’s relative motion by extracting and matching features captured by the RGB-D camera from the environment. The state observer of the RGB-D visual-aided inertial navigation was then designed based on the invariant observer theory for systems possessing symmetries. The motion estimates from the RGB-D VO were fused with inertial and magnetic measurements from the onboard MEMS sensors via the state observer, providing the MAV with accurate estimates of its full six degree-of-freedom states. Implementations on a quadrotor MAV and indoor flight test results demonstrate that the resulting state observer is effective in estimating the MAV’s states without relying on external navigation aids such as GPS. The properties of computational efficiency and simplicity in gain tuning make the proposed invariant observer-based navigation scheme appealing for actual MAV applications in indoor environments.

Integrated Indoor Navigation System for Ground Vehicles With Automatic 3-D Alignment and Position Initialization

This paper introduces an autonomous integrated indoor navigation system for ground vehicles that fuses inertial sensors, light detection and ranging (LiDAR) sensors, received signal strength (RSS) observations in wireless local area networks (WLANs), odometry, and predefined occupancy floor maps. This paper proposes a solution for the problem of automatic self-alignment and position initialization indoors under the absence of an absolute navigation system such as Global Navigation Satellite Systems (GNSS). The initial tilt angles (roll and pitch) are estimated by an extended Kalman filter (EKF) that uses two horizontal accelerometers as measurements. The initial position and heading estimation is performed using a subimage matching algorithm based on normalized cross-correlation between projected 2-D LiDAR scans and an occupancy floor map of the environment. The ambiguities in position/heading initialization are resolved using RSS. The proposed position/heading estimation module is also utilized in navigation mode as a source of absolute position/heading updates to EKF for enhanced observability. The state predictor is an enhanced 3-D inertial navigation system that utilizes low-cost microelectromechanical system (MEMS)-based reduced inertial sensor set aided by vehicle odometry. In navigation mode, LiDAR scans are used to estimate the vehicle's relative motions using an inertial-aided iterative closest point algorithm. To fuse all available measurements, a multirate multimode EKF design is proposed to correct navigation states and estimate sensor biases. The developed system was tested under a real indoor office environment covered by an IEEE 802.11 WLAN on a mobile robot platform equipped with MEMS inertial sensors, a WLAN interface, a 2-D LiDAR scanner, and a quadrature encoder. Results demonstrated the capabilities of the self-alignment and initialization module and showed average submeter-level positioning accuracy.

A novel optimal configuration form redundant MEMS inertial sensors based on the orthogonal rotation method.

In order to improve the accuracy and reliability of micro-electro mechanical systems (MEMS) navigation systems, an orthogonal rotation method-based nine-gyro redundant MEMS configuration is presented. By analyzing the accuracy and reliability characteristics of an inertial navigation system (INS), criteria for redundant configuration design are introduced. Then the orthogonal rotation configuration is formed through a two-rotation of a set of orthogonal inertial sensors around a space vector. A feasible installation method is given for the real engineering realization of this proposed configuration. The performances of the novel configuration and another six configurations are comprehensively compared and analyzed. Simulation and experimentation are also conducted, and the results show that the orthogonal rotation configuration has the best reliability, accuracy and fault detection and isolation (FDI) performance when the number of gyros is nine.

A Pedestrian Dead Reckoning System Integrating Low - Cost MEMS Inertial Sensors and GPS Receiver

The body-mounted inertial systems for pedestrian navigation do not require any preinstalled facilities and can run autonomously. The advantages over other technologies make it especially attractive for the applications such as first responders, military and consumer markets. The hardware platform integrating the low-cost, low-power and small-size MEMS (micro-electro-mechanical systems) inertial sensors and GPS (global positioning system) receiver is proposed. When the satellite signals are available, the location of the pedestrian is directly obtained from the GPS receiver. The inertial sensors are the complement of the GPS receiver in places where the GPS signals are not available, such as indoors, urban canyons and places under dense foliages. The height tracking is achieved by the barometer. The proposed PDR (pedestrian dead reckoning) algorithm is real-timely implemented in the platform. The simple but effective step detection and step length estimation method are realized to reduce the computation and memory requirements on the microprocessor. A complementary filter is proposed to fuse the data from the accelerometer, gyroscope and digital compass for decreasing the heading error, which is the main error source in positioning. The reliability and accuracy of the proposed system is verified by field pedestrian walking tests in outdoors and indoors. The positioning error is less than 4% of the total traveled distance. The results indicate that the pedestrian dead reckoning system is able to provide satisfactory tracking performance.

2D Human Gesture Tracking and Recognition by the Fusion of MEMS Inertial and Vision Sensors

In this paper, we present an algorithm for hand gesture tracking and recognition based on the integration of a custom-built microelectromechanical systems (MEMS)-based inertial sensor (or measurement unit) and a low resolution imaging (i.e., vision) sensor. We discuss the 2-D gesture recognition and tracking results here, but the algorithm can be extended to 3-D motion tracking and gesture recognition in the future. Essentially, this paper shows that inertial data sampled at 100 Hz and vision data at 5 frames/s could be fused by an extended Kalman filter, and used for accurate human hand gesture recognition and tracking. Since an inertial sensor is better at tracking rapid movements, while a vision sensor is more stable and accurate for tracking slow movements, a novel adaptive algorithm has been developed to adjust measurement noise covariance according to the measured accelerations and the angular rotation rates. The experimental results verify that the proposed method is capable of reducing the velocity error and position drift in an MEMS-based inertial sensor when aided by the vision sensor. Compensating for the time delay due to the visual data processing cycles, a moving average filter is applied to remove the high frequency noise and propagate the inertial signals. The reconstructed trajectories of the first 10 Arabic numerals are further recognized using dynamic time warping with a direct cosine transform for feature extraction, resulting in an accuracy of 92.3% and individual numeral recognition within 100 ms.

Implementation and Performance of a GPS/INS Tightly Coupled Assisted PLL Architecture Using MEMS Inertial Sensors

The use of global navigation satellite system receivers for navigation still presents many challenges in urban canyon and indoor environments, where satellite availability is typically reduced and received signals are attenuated. To improve the navigation performance in such environments, several enhancement methods can be implemented. For instance, external aid provided through coupling with other sensors has proven to contribute substantially to enhancing navigation performance and robustness. Within this context, coupling a very simple GPS receiver with an Inertial Navigation System (INS) based on low-cost micro-electro-mechanical systems (MEMS) inertial sensors is considered in this paper. In particular, we propose a GPS/INS Tightly Coupled Assisted PLL (TCAPLL) architecture, and present most of the associated challenges that need to be addressed when dealing with very-low-performance MEMS inertial sensors. In addition, we propose a data monitoring system in charge of checking the quality of the measurement flow in the architecture. The implementation of the TCAPLL is discussed in detail, and its performance under different scenarios is assessed. Finally, the architecture is evaluated through a test campaign using a vehicle that is driven in urban environments, with the purpose of highlighting the pros and cons of combining MEMS inertial sensors with GPS over GPS alone.

De‐noising MEMS inertial sensors for low‐cost vehicular attitude estimation based on singular spectrum analysis and independent component analysis

A novel method to de-noise microelectromechanical system (MEMS) inertial sensors by cascaded integration of singular spectrum analysis (SSA) and independent component analysis (ICA) is proposed to improve the attitude accuracy for low-cost attitude estimation. The low-frequency vibration noise and acceleration disturbance induce large errors to attitude estimation since MEMS accelerometers provide attitude measurement for sensor fusion by measuring the gravity vector. It is proposed to remove the low-frequency vibration noise by SSA and to mitigate the acceleration disturbance by ICA. SSA can effectively separate the trend and periodic vibration noise and ICA can effectively extract the acceleration disturbance with the help of turning rate measured by the yaw gyro. The proposed technique was tested on real road experiments showing significant improvement of attitude accuracy.

Precise air time determination of athletic jumps with low-cost MEMS inertial sensors using multiple attribute decision making

Commercial devices for real-time sports performance measurement are available. The small size and low cost of micro-electro-mechanical systems (MEMS) inertial measurement units (IMUs) are exploited in these devices to make their body mountable and affordable for the general public. These devices are widely used in sports such as skiing, snowboarding and mountain biking to measure the air time (AT) of aerial athletic jumps in real time. AT is considered as an important performance variable. However, the performance of currently used real-time athletic jump AT determination algorithms using MEMS IMUs are unsatisfactory in terms of accuracy and reliability. In this study, a new algorithm for AT determination with a head-mounted MEMS IMU is proposed. The algorithm has a probabilistic approach using multiple attribute decision making. The concept of jump characteristic points and a novel method used in this algorithm, namely preceding and following acceleration difference, are introduced. The field experiment ...

Performance Analysis of Alignment Process of MEMS IMU

The procedure of determining the initial values of the attitude angles (pitch, roll, and heading) is known as the alignment. Also, it is essential to align an inertial system before the start of navigation. Unless the inertial system is not aligned with the vehicle, the information provided by MEMS (microelectromechanical system) sensors is not useful for navigating the vehicle. At the moment MEMS gyroscopes have poor characteristics and it’s necessary to develop specific algorithms in order to obtain the attitude information of the object. Most of the standard algorithms for the attitude estimation are not suitable when using MEMS inertial sensors. The wavelet technique, the Kalman filter, and the quaternion are not new in navigation data processing. But the joint use of those techniques for MEMS sensor data processing can give some new results. In this paper the performance of a developed algorithm for the attitude estimation using MEMS IMU (inertial measurement unit) is tested. The obtained results are compared with the attitude output of another commercial GPS/IMU device by Xsens. The impact of MEMS sensor measurement noises on an alignment process is analysed. Some recommendations for the Kalman filter algorithm tuning to decrease standard deviation of the attitude estimation are given.

Accuracy improvement of GPS/MEMS-INS integrated navigation system during GPS signal outage for land vehicle navigation

To improve the reliability and accuracy of the global positioning system (GPS)/micro electromechanical system (MEMS)-inertial navigation system (INS) integrated navigation system, this paper proposes two different methods. Based on wavelet threshold denoising and functional coefficient autoregressive (FAR) modeling, a combined data processing method is presented for MEMS inertial sensor, and GPS attitude information is also introduced to improve the estimation accuracy of MEMS inertial sensor errors. Then the positioning accuracy during GPS signal short outage is enhanced. To improve the positioning accuracy when a GPS signal is blocked for long time and solve the problem of the traditional adaptive neuro-fuzzy inference system (ANFIS) method with poor dynamic adaptation and large calculation amount, a self-constructive ANFIS (SCANFIS) combined with the extended Kalman filter (EKF) is proposed for MEMS-INS errors modeling and predicting. Experimental road test results validate the efficiency of the proposed methods.

Study of the characteristics of random errors in measurements by MEMS inertial sensors

This paper considers a stochastic analysis of measurement errors in MotionNode inertial measurement units (IMUs). The sensors of this device are based on the microelectromechanical system (MEMS) technology. The use of the Allan variance algorithm for identifying the error’s nature and the stochastic modeling of this error make it possible to substantially reduce the measurement errors by sensors. Based on the results of this study, we elaborated a number of recommendations for increasing the efficiency of the use of MEMS inertial sensors for problems of the navigation of moving objects.

MEMS INS/GPS Data Fusion using Particle Filter

Coupling GPS with Micro-Electro-Mechanical Systems (MEMS) Inertial Navigation Systems (INS) is an challenging way of improving land vehicle navigation performance. MEMS inertial sensors suffer from complex stochastic errors, which are difficult to compensate and model using conventional Kalman Filter, as it provides an effective solution to the linear Gaussian filtering problem. However where there is nonlinearity, either in the model specification or observation process, other method are required. Particle filtering techniques are good candidates to solve the corresponding nonlinear estimation problem associated to MEMS INS/GPS hybridization. Here nonlinear models for accelerometer were successfully implemented using particle filter (PF) . The performance of the resulting algorithm was illustrated through experimental results. Also the position estimation results using PF during GPS signal outages were presented. Ill. 4, bibl. 5 (in English; abstracts in English and Lithuanian). http://dx.doi.org/10.5755/j01.eee.112.6.450

Parametrically Amplified $Z$-Axis Lorentz Force Magnetometer

This paper describes a microelectromechanical systems (MEMS) Lorentz force navigation magnetometer whose fabrication is fully compatible with existing foundry processes used to manufacture MEMS inertial sensors. Operating at 1 atm, the magnetometer is shown to have a Brownian-noise-limited field resolution of 87 nT/√Hz and a corresponding angular resolution of 0.7°/√Hz. To achieve Brownian-noise-limited performance at 1 atm, parametric amplification was performed which amplifies the Lorentz force 40% more than the Brownian noise force and increases the field sensitivity of the device up to 82.5 folds. Here, the parametric signal is also used to perform electromechanical amplitude modulation to mitigate capacitive feedthrough of the drive signal to the sensing electronics.

Tightly Coupled Low Cost 3D RISS/GPS Integration Using a Mixture Particle Filter for Vehicular Navigation

Satellite navigation systems such as the global positioning system (GPS) are currently the most common technique used for land vehicle positioning. However, in GPS-denied environments, there is an interruption in the positioning information. Low-cost micro-electro mechanical system (MEMS)-based inertial sensors can be integrated with GPS and enhance the performance in denied GPS environments. The traditional technique for this integration problem is Kalman filtering (KF). Due to the inherent errors of low-cost MEMS inertial sensors and their large stochastic drifts, KF, with its linearized models, has limited capabilities in providing accurate positioning. Particle filtering (PF) was recently suggested as a nonlinear filtering technique to accommodate for arbitrary inertial sensor characteristics, motion dynamics and noise distributions. An enhanced version of PF called the Mixture PF is utilized in this study to perform tightly coupled integration of a three dimensional (3D) reduced inertial sensors system (RISS) with GPS. In this work, the RISS consists of one single-axis gyroscope and a two-axis accelerometer used together with the vehicle’s odometer to obtain 3D navigation states. These sensors are then integrated with GPS in a tightly coupled scheme. In loosely-coupled integration, at least four satellites are needed to provide acceptable GPS position and velocity updates for the integration filter. The advantage of the tightly-coupled integration is that it can provide GPS measurement update(s) even when the number of visible satellites is three or lower, thereby improving the operation of the navigation system in environments with partial blockages by providing continuous aiding to the inertial sensors even during limited GPS satellite availability. To effectively exploit the capabilities of PF, advanced modeling for the stochastic drift of the vertically aligned gyroscope is used. In order to benefit from measurement updates for such drift, which are loosely-coupled updates, a hybrid loosely/tightly coupled solution is proposed. This solution is suitable for downtown environments because of the long natural outages or degradation of GPS. The performance of the proposed 3D Navigation solution using Mixture PF for 3D RISS/GPS integration is examined by road test trajectories in a land vehicle and compared to the KF counterpart.

Reliable jump detection for snow sports with low-cost MEMS inertial sensors

Body-mounted devices, incorporating low-cost micro-electromechanical systems (MEMS) Inertial Measurement Units (IMUs), for real-time sports performance feedback are commercially available. In sports such as skiing, snowboarding, and mountain biking, aerial jumps can be detected with these devices and performance variables including air time and jump drop can be calculated real-time. However, the performance of currently used real-time athletic jump detection algorithms using MEMS IMUs is unsatisfactory in terms of accuracy, power efficiency, and reliability. In this paper, a novel algorithm for jump detection with a head-mounted MEMS IMU is proposed. Two novel methods used in this algorithm, namely Windowed Mean Canceled Multiplication and Preceding and Following Acceleration Difference, are introduced. Field experiments are conducted and the results of the proposed algorithm are compared with those of algorithms used in two state-of-the-art sport performance measurement devices. Results demonstrate that the proposed jump detection algorithm comprehensively outperforms these commercial algorithms.

A Local Positioning System for Loader Cranes Based on Wireless Sensors—A Feasibility Study

This paper presents a feasibility study that deals with a local positioning system for a loader crane based on battery-powered wireless sensors and consists of two joint angular sensors and one telescopic boom length ranger. The practical challenges associated with using conventional sensors are described in order to provide the motivation behind the choice to use the sensing methods proposed in this paper. A novel method is tested using microelectromechanical system inertial sensors mounted around the crane joints to indirectly measure the joint angles, as well as an ultrasound time-of-flight ranging method to measure the telescopic boom length. The local positioning system's wireless sensor prototype designs are described in detail. Data from the angular sensor experiments conducted on a loader crane and the ultrasound ranging experiments conducted both in the laboratory and on the loader crane are presented and analyzed. The preliminary results from these experiments show that the performance of the new sensors is promising. The conclusion is drawn from the experimental results, and future work for this local positioning system is also described.

Experimental Results on an Integrated GPS and Multisensor System for Land Vehicle Positioning

Global position system (GPS) is being widely used in land vehicles to provide positioning information. However, in urban canyons, rural tree canopies, and tunnels, the GPS satellite signal is usually blocked and there is an interruption in the positioning information. To obtain positioning solution during GPS outages, GPS can be augmented with an inertial navigation system (INS). However, the utilization of full inertial measurement unit (IMU) in land vehicles could be quite expensive despite the use of the microelectromechanical system (MEMS)‐based sensors. Contemporary research is focused on reducing the number of inertial sensors inside an IMU. This paper explores a multisensor system (MSS) involving single‐axis gyroscope and an odometer to provide full 2D positioning solution in denied GPS environments. Furthermore, a Kalman filter (KF) model is utilized to predict and compensate the position errors of the proposed MSS. The performance of the proposed method is examined by conducting several road tests trajectories using both MEMS and tactical grade inertial sensors. It was found that by using proposed MSS algorithm, the positional inaccuracies caused by GPS signal blockages are adequately compensated and resulting positional information can be used to steer the land vehicles during GPS outages with relatively small position errors.

Characterizing Stability and Control of Subscale Aircraft from Wind-Tunnel Dynamic Motion

This paper outlines a process of determining the stability and control characteristics of small-scale aircraft from wind-tunnel experiments which aim to emulate flight tests. Scaled aircraft models with representative control surfaces are flown in semifree flight on a test rig allowing motion in 4 degrees of freedom. The motion is measured and system identification and parameter estimation techniques are used to obtain a mathematical description of the dynamics of the model. Previous work has relied on the use of potentiometers to measure the model's attitude about its pivot point with other motion variables being generated analytically using these measurements and well-known kinematic relationships. However, it is now possible to mount sensors within the model as advances in technology have produced microelectromechanical system packages of small size, lightweight, low cost, and low power consumption. The current work is investigating the effectiveness of using microelectromechanical system inertial sensors to measure motion of the model. An example of the system identification process carried out on a one-twelfth scale BAe Hawk model is also presented. Parameter estimation is performed in the frequency domain using the equation error and output error methods.

Out with the old in with the new

Abstract The materials issues facing the microelectromechanical systems (MEMS) community can be understood best in terms of the historical context. The field began almost as an afterthought among those engaged in integrated circuit production. These researchers recognized early on that the same processes used in the production of circuits could be re-ordered to make very small mechanical devices. The huge investment made by the electronics community in silicon technologies — and the relative ease with which these techniques could be adapted to device production — made them an obvious resource for early MEMS designers. It is no accident that polycrystalline silicon is the most commonly used structural material. But with the continued expansion of MEMS devices into new areas of application, the limitations of silicon (Si) usefulness became clear. The need to combine electronics and MEMS on the same chip ( iMEMS ), improve the wear characteristics of moving parts, and achieve a greater mass of moving parts in MEMS inertial sensors have led researchers away from a ‘one-material-fits-all’ approach. Instead the search is on for materials that more directly serve specific ends. The need for biocompatibility in the emerging field of bio-MEMS has added urgency to the quest for new materials, since Si-based materials cannot meet every bio-MEMS need.