Performance Analysis of Alignment Process of MEMS IMU

In a deeply-coupled GPS/INS integrated system, the use of the inertial aiding information can improve the tracking loop performance and make the system more robust. To meet this requirement, the inertial aiding information should have sufficient accuracy in short-term (such as the sampling interval of GPS, e.g. 1sec). The MEMS (Micro-Electro Mechanical System) IMU (Inertial Measurement Unit) can be a promising candidate due to its small size and low cost. There should be no doubt that MEMS INS (Inertial Navigation System) can aid the GPS receiver tracking loop by eliminating the dominant part of the motion dynamic stress, considering that the INS errors induced by the receiver motion dynamics is much less than the motion dynamic itself, when the receiver manoeuvres. So the only concern the side effect caused by MEMS INS, which determine whether MEMS IMU is qualified for deep integration, is its navigation error independent with the motion dynamics (i.e. manoeuvre-independent error). This paper assesses this side effect of MEMS INS in terms of providing Doppler aiding data in to the GPS carrier tracking loop through a thorough error propagation analysis. The Laplace transform analysis is applied to the simplified INS error dynamic equations under stationary condition and find out the transfer relation between the error sources and the velocity estimation errors. Then the velocity error is converted to Doppler aiding error and substitute into the GPS tracking loop to analyze the corresponding carrier phase error. Results show that the largest velocity error caused by maneuver-independent errors is less than 0.1m/s during the typical GPS update interval (e.g. 1 sec), which meets the real road test results. The consequent carrier phase tracking error caused by the maneuver-independent error of MEMS INS is below 1.2 degree, which is much less than receiver inherent errors (e.g. the oscillator error and thermal noise). Conclusion can be reached that even the low-end MEMS IMUs have the ability of aiding the GPS receiver signal tracking although it induces some additional errors.

Inertial Measurement Units (IMUs) using MEMS (Micro Electro Mechanical Systems) sensors, to detect rotation and acceleration, are extremely sensitive to vibration and shock. Because these IMUs are used in aircraft, missiles, and even in gun launched projectiles, the vibration and shock levels can be extremely high. We have been successful in designing isolation systems that protect the MEMS sensors from these harsh environments. These isolation systems attenuate the shock and vibration in frequency ranges that would otherwise degrade IMU performance and even damage the sensors. In designing an isolation system, for an IMU with MEMS devices, the designer must first acquire a good definition of the shock and vibration environment to which the IMU will be exposed. Then the designer must determine the frequencies and levels to which the MEMS devices are sensitive. Finally the designer must have knowledge of IMU properties such as resonant frequencies, mass properties, clock frequencies, etc. By knowing all of this information, and how it affects IMU performance, the designer can determine the proper parameters for the isolation system. A well designed isolation system using these parameters will be capable of protecting the MEMS devices while maintaining IMU performance through out all specified environments.

Estimating wind speed and direction in outdoor areas with high precision and resolution requires a large number of anemometers distributed in different locations. Compared with others conventional sensors, the micro electro mechanical system (MEMS) sensors are lighter and smaller, therefore, they can be integrated in large numbers. Moreover, due to their batch production, MEMS sensors are relatively low cost. Their main drawbacks are the dependence on the air temperature and the inability of detecting the flow direction. This paper deals with the design of a cylindrical-shaped probe equipped with two dual MEMS hot-film sensor chips calibrated in a wind tunnel. With the obtained results, a model based on artificial neural network is developed and implemented on Arduino board. This model is assigned to calculate the corresponding values of wind speed and direction with correcting the effect of temperature drift. Finally, outdoor tests are carried out at wind monitoring points measured by an opto-electronic anemometer and an opto-electronic wind vane. The obtained results are accurate and agree well.

Autonomous landing system is an important part of unmanned aerial vehicle (UAV) autonomous flight. Large fixed‐wing UAVs often consume more onboard space and computing resources. With the improvement of system integration and the reduction of high‐performance global navigation satellite system (GNSS) costs, real‐time kinematic (RTK) GNSS has been widely used in small‐scale and low‐cost fixed‐wing UAVs. This article uses less onboard hardware and ground facility requirements to design an autonomous taking off and landing system suitable for general fixed‐wing UAVs. A modified gradient descent attitude estimator, based on microelectromechanical system (MEMS) inertial measurement unit (IMU) and RTK GNSS, and a guidance method based on a tracking differentiator for using in a high dynamic environment are proposed, and circular path following and vertical transient simulations are performed. The simulation results show that this method can improve attitude estimation accuracy, motion control accuracy, and altitudinal transient quality. Finally, the actual flight test verifies its feasibility in the actual environment. The flight test results prove that it is feasible to implement the autonomous landing process of fixed‐wing UAV with the proposed system and method.

Smart reinforcement steel bars with low-cost MEMS sensors for the structural health monitoring of RC structures

This paper presents a navigation grade micro-electromechanical system (MEMS) inertial measurement unit (IMU) that was successfully applied for the first time in the Lobster-Eye X-ray Satellite in July 2020. A six-axis MEMS gyroscope redundant configuration is adopted in the unit to improve the performance through mutual calibration of a set of two-axis gyroscopes in the same direction. In the paper, a satisfactory precision of the gyroscope is achieved by customized and self-calibration gyroscopes whose parameters are adjusted at the expense of bandwidth and dynamics. According to the in-orbit measured data, the MEMS IMU provides an outstanding precision of better than 0.02 °/h (1σ) with excellent bias instability of 0.006 °/h and angle random walk (ARW) of around 0.003 °/h1/2. It is the highest precision MEMS IMU for commercial aerospace use ever publicly reported in the world to date.

In this paper, we propose a novel fault diagnosis (FD) approach for micro-electromechanical systems (MEMS) inertial sensors that recognize the fault patterns of MEMS inertial sensors in an end-to-end manner. We use a convolutional neural network (CNN)-based data-driven method to classify the temperature-related sensor faults in unmanned aerial vehicles (UAVs). First, we formulate the FD problem for MEMS inertial sensors into a deep learning framework. Second, we design a multi-scale CNN which uses the raw data of MEMS inertial sensors as input and which outputs classification results indicating faults. Then we extract fault features in the temperature domain to solve the non-uniform sampling problem. Finally, we propose an improved adaptive learning rate optimization method which accelerates the loss convergence by using the Kalman filter (KF) to train the network efficiently with a small dataset. Our experimental results show that our method achieved high fault recognition accuracy and that our proposed adaptive learning rate method improved performance in terms of loss convergence and robustness on a small training batch.

Commonly encountered problems in the manipulation of objects with robotic hands are the contact force control and the setting of approaching motion. Microelectromechanical systems (MEMS) sensors on robots offer several solutions to these problems along with new capabilities. In this review, we analyze tactile, force and/or pressure sensors produced by MEMS technologies including off-the-shelf products such as MEMS barometric sensors. Alone or in conjunction with other sensors, MEMS platforms are considered very promising for robots to detect the contact forces, slippage and the distance to the objects for effective dexterous manipulation. We briefly reviewed several sensing mechanisms and principles, such as capacitive, resistive, piezoresistive and triboelectric, combined with new flexible materials technologies including polymers processing and MEMS-embedded textiles for flexible and snake robots. We demonstrated that without taking up extra space and at the same time remaining lightweight, several MEMS sensors can be integrated into robotic hands to simulate human fingers, gripping, hardness and stiffness sensations. MEMS have high potential of enabling new generation microactuators, microsensors, micro miniature motion-systems (e.g., microrobots) that will be indispensable for health, security, safety and environmental protection.

In the ultra-tight integration, the signal processing of GNSS receiver baseband can be enhanced by the aiding of INS information. With the purpose of improving the performance of ultra-tight integration with low-cost MEMS (Micro-Electro-Mechanical System) IMU (Inertial Measurement Unit), the paper explores a deeply coupled integration scheme of GNSS and the software defined IMU (SDI) based on vibratory MEMS rate gyroscope. The error of vibratory MEMS rate gyroscope demodulated by peak detection technique is interpreted in the gyroscope signal domain and the gyroscope demodulation parameter deviations are estimated by the integration filter and adjusted accordingly. The presented integration scheme is tested by an integrated navigation system which consists of a software defined GNSS receiver, a single-axis vibratory MEMS gyroscope GI-CVG-N2100A based SDI and an Xsens MEMS IMU. The single-axis vibratory MEMS gyroscope GI-CVG-N2100A in the test, is employed to replace one of the gyroscope axes of the Xsens MEMS IMU.

Due to the need for providing low-cost, small-size, and high-reliability calibration solution around the clock independently, the Micro-Electro-Mechanical System (MEMS) inertial measurement unit (IMU) is a hot topic currently. In this paper, we propose a new calibration method based on MEMS IMU with the assistance of the FOG IMU. Different from the traditional MEMS IMU calibration, a Four-Position-and-Three-Rotation (FPTR) calibration sequence is designed in the conditions of lacking turntable. The simulation result shows that all 24 calibration parameters converge through the calibration rotation scheme. An experiment result of qualitative analysis is presented, by comparing the traditional method and proposed calibration method, which reveals the repeatability and stability of the proposed method. In practical applications, this FPTR calibration method can effectively reduce the calibration time and improve the experimental efficiency. In summary, the proposed method is suitable for the mass production calibration of MEMS IMU, and possesses the ability to calibrate full parameters of MEMS IMU at once without turntable or other devices. The research outcomes can provide a practical basis for MEMS IMU plus FOG IMU calibration.

Small, lightweight, power-efficient and low-cost microelectromechanical system (MEMS) inertial sensors and microcontrollers available in the market today help reduce the instability of Multibeam Sonars. Current MEMS inertial measurement units (IMUs) come in many shapes, sizes, and costs - depending on the application and performance required. Although MEMS inertial sensors offer affordable and appropriately scaled units, they are not currently capable of meeting all requirements for accurate and precise attitudes, due to their inherent measurement noise. The article presents the comparison of different MEMS technologies and their parameters regarding to the main application, namely Multibeam Echo Sounders (MBES). The quality of MEMS parameters is crucial for further MBES record-processing. The article presents the results of undertaken researches in that area, and these results are relatively positive for low-cost MEMS. The paper undertakes some vital aspect of using MEMS in the attitude and heading reference system (AHRS) context. The article presents a few aspects of MEMS gyro errors and their estimation process in the context of INS processing flow, as well as points out the main difficulties behind the INS when using a few top MEMS technologies.

Due to the advantages of small size and low cost, micro-electro-mechanical system (MEMS) inertial navigation systems (INS) show good prospects for use as a part of measurement-while-drilling (MWD) equipment to guarantee proper directional drilling procedure. Since current MEMS sensors have inaccurate measurements, an update aiding solution is developed using the Kalman filtering (KF) technique. However, because of the inherent poor behavior of MEMS sensors, KF technique with its linearized models has limited capability in providing accurate solution through the entire surveying process. In addition, certain realistic problems from the rugged environment would interrupt the updates in KF, without which the performance of the inertial system would deteriorate badly. This research proposes a fast orthogonal search (FOS)/KF solution where the FOS (a nonlinear modeling technique) method is proposed to augment KF. The experimental results illustrate that the FOS/KF solution outperforms the KF-only solution. Velocity and position performance are greatly enhanced during the interruptions of measurement updates.

A signal shift following solder reflow or temperature cycle stress testing can severely affect micro-electro-mechanical systems (MEMS) product performance in encapsulated package. This is mainly attributed to nonlinear material property of the mold compound, a key low cost packaging material. Due to viscoelastic characteristic, the mold compound exhibits a temperature and time dependent behavior. Above the temperature of glass transition (Tg) of the mold compound, the viscoelastic behavior is more significant. Thus, when the MEMS package is subjected to high temperature such as solder reflow process, a package induced internal stress on the MEMS sensor is generated depending on the maximum temperature reached and cooling rate. This results in the signal shift. Minimizing this signal shift plays an important role in MEMS product quality and performance. In this study, high Tg mold compounds (Tg: 200°C) were evaluated to alleviate the signal shift. Material property characterization tests were performed and test vehicles were assembled with the selected molding compounds. The MEMS sensor signal was measured before and after the temperature cycle test. Also, the viscoelastic material property of the mold compound was used in finite element analysis to investigate the resulting deformation and stress of the MEMS sensor. Compared to a baseline mold compound (Tg: 110°C), the high Tg molding compound did show about 10% less signal shift in the temperature range of -40°C to 125°C.

Flexible and composite structures often develop hidden damages, e.g. cracks or delamination between laminae or phases. Such events could be sensed through embedded structural health monitoring (SHM) systems, but past experimental studies in the literature proved that embedding sensors for SHM purposes may decrease the reliability of the structure, as the sensor acts as an inclusion. In former studies, the authors proposed to adopt a surface-mounted SHM approach based on inertial MEMS (micro electro-mechanical systems) sensors, which has two advantages: it is low cost and also it avoids the afore-mentioned detrimental effects on the endurance limit state of the structure. However, the low accuracy of the MEMS sensors and the type of response that they can measure may hinder an effective monitoring of the structural state; this can be overcome through redundancy and an efficient sensor placement. In this article, an automated approach is discussed for obtaining the optimal topology of a sparse MEMS sensor network. In this regard, the scenarios are assumed unknown in terms of extent and location of stiffness degradation due to damage. The optimal sensor locations are obtained via maximization of the global sensitivity of the measurements to damage. The method can be also implemented in a multi-scale framework, to efficiently handle (micro) sensors, (meso) damaged regions and (macro) structural components of (by far) different sizes. Data related to composite specimens and panels are discussed, with the aim of assessing the identifiability of damage through self-adapting theoretical/numerical (digital) twins of the monitored structures.

This paper describes the use of a low-cost standalone MEMS-IMU (micro-electromechanical system - inertial measurement unit) sensor system developed by the National Research Council Canada (NRC) for manoeuvre recognition in helicopters. The system records accelerations, angular rotation rates, magnetic flux, altitude, location and velocity through its IMU and GPS. The MEMS-IMU system was flown on the Bell 412 CH-146 Griffon helicopter in a series of scripted flights consisting of 60 manoeuvres and regimes from the helicopter's usage spectrum. A flight log recorded by passengers with detailed start and stop times and identification of the manoeuvres during flight was key information in the development of manoeuvre recognition models. Statistical tests to analyze the data diversity between each flight showed significant variability of the parameters from flight to flight. Because of this variability and because there was significant imbalance in the distribution of data for the individual manoeuvres, a pooled stratified sampling scheme was used to construct a representative training set for developing data-driven models. Different subsets of the MEMSIMU measurements were explored to exclude GPS and/or magnetometer readings. Even with the different subsets, the classifier results using stratified sampling show that very high overall classification accuracy can be obtained using the measurements from the standalone MEMS-IMU sensor system.