Microelectromechanical Inertial Measurement Unit Research Articles

Effects of repetitive low-acceleration impacts on attitude estimation with micro-electromechanical inertial measurement units.

Inertial Measurement Units are present in several applications in aerospace, unmanned vehicle navigation, legged robots, and human motion tracking systems, due to their ability to estimate a body's acceleration, orientation and angular rate. In contrast to rovers and drones, legged locomotion involves repeated impacts between the feet and the ground, and rapid locomotion (e.g., running) involves alternating stance and flight phases, resulting in substantial oscillations in vertical acceleration. The aim of this research is to investigate the effects of periodic low-acceleration impacts (4 g, 8 g and 16 g), which imitate the vertical motion of a running robot, on the attitude estimation of multiple Micro-Electromechanical Systems IMUs. The results reveal the presence of a significant drift in the attitude estimation of the sensors, which can provide important information during the design process of a robot (sensor selection), or during the control phase (e.g., the system will know that after a series of impacts the attitude estimations will be inaccurate).

ANALYSIS OF MICRO-ELECTROMECHANICAL INERTIAL MEASUREMENT UNITS FOR UNMANNED AERIAL VEHICLE APPLICATIONS

Typically, an inertial navigation system (INS) is used to determine the position, speed, and orientation of an object moving relative to the earth's surface. The navigation information (position, speed and orientation) of an unmanned aerial vehicle (UAV) is needed to control its flight. Since the resistance of INS to interferences is very high, it is possible to ensure reliable flights in conditions of high-intensity noise. This article explores the principles of constructing inertial measurement units (IMU) that are part of the INS and indicates perspective directions for their development. Micro-electromechanical inertial measurement units were studied in this work, and functional and principal electrical circuits for connecting units of inertial measurements to the microcontroller were developed. The results of practical measurements of units without calibration and after calibration were obtained using the created laboratory device. Based on the obtained results, the necessity of sensor calibration was revealed, and accuracy was improved by performing calibration with the Kalman filter algorithm. The Kalman filter is the heart of the navigation system. In a low-cost system, IMU errors like bias, scale factor error and random walk noise dominate the INS error growth.

Research on the Shearer Positioning Method Based on the MEMS Inertial Sensors/Odometer Integrated Navigation System and RTS Smoother.

The shearer positioning method with an inertial measurement unit and the odometer is feasible in the longwall coal-mining process. However, the positioning accuracy will continue to decrease, especially for the micro-electromechanical inertial measurement unit (MIMU). In order to further improve the positioning accuracy of the shearer without adding other external sensors, the positioning method of the Rauch-Tung-Striebel (RTS) smoother-aided MIMU and odometer is proposed. A Kalman filter (KF) with the velocity and position measurements, which are provided by the odometer and closing path optimal estimation model (CPOEM), respectively, is established. The observability analysis is discussed to study the possible conditions under which the error states of KF can be estimated. A RTS smoother with the above-mentioned KF as the forward filter is built. Finally, the experiments of simulating the movement of the shearer through a mobile carrier were carried out, with a longitudinal movement distance of 44.6 m and a lateral advance distance of 1.2 m. The results show that the proposed method can effectively improve the positioning accuracy. In addition, the odometer scale factor and mounting angles can be estimated in real time.

A redundant fused MIMU attitude system algorithm based on two-stage data fusion of MEMS gyro clusters array

A redundant fused microelectromechanical inertial measurement unit (MIMU) attitude system composed of a two-stage data fusion is proposed. In the first stage, multiple gyroscopes are arranged on the axes of the MIMU, and a KF is presented to fuse measurements of the gyro arrays. In the second stage, an integrated KF is designed to estimate attitude quaternion, in which the Gauss-Newton algorithm is used to estimate quaternion to construct KF measurements. Lastly, a fused MIMU with three eight-gyro arrays is developed. The results show that the ARW and RRW are reduced by a factor of about 4.86 and 3.8 respectively, and the quaternion error solved by the fused signals is smaller than that by the original MIMU signals. Additionally, the 1σ error of quaternion components q0, q1 and q3 are each reduced by half while q2 is reduced to 13% of the corresponding error using the original MIMU signals.

A New MIMU/GNSS Ultra-Tightly Coupled Integration Architecture for Mitigating Abrupt Changes of Frequency Tracking Errors.

We present a new ultra-tightly coupled (UTC) integration architecture of a micro-electromechanical inertial measurement unit (MIMU) and global navigation satellite system (GNSS) to reduce the performance degradation caused by abrupt changes of frequency tracking errors. A large frequency error will lead to a decrease in the carrier-to-noise ratio (C/N0) estimate and an increase in the code discriminator estimation error. The disruptive effects of frequency errors on the estimation of C/N0 and on the code discriminator are quantitatively evaluated via theoretical analyses and Monte Carlo simulations. The new MIMU/GNSS UTC architecture introduces a large frequency error detector and a refined frequency processor based on a retuned frequency in each tracking channel. In addition, an adaptive channel prefilter with multiple fading factors is introduced as an alternate to the conventional prefilter. Numerical simulations based on a highly dynamic trajectory are used to assess performance. The simulation results show that when there is an abrupt step change in the frequency tracking error, the new UTC architecture can effectively suppress the divergence of navigation solutions and the loss of tracking lock, and can significantly reduce the deviation of the C/N0 estimation.

Сравнительный анализ микроэлектромеханических инерциальных измерительных модулей различных производителей для снятия двигательной активности человека

Сравнительный анализ микроэлектромеханических инерциальных измерительных модулей различных производителей для снятия двигательной активности человека

Accurate attitude estimation of HB2 standard model based on QNCF in hypersonic wind tunnel test

The accuracy of the HB2 standard model attitude measurement has an important impact on the hypersonic wind tunnel data assessment. The limited size of the model and the existence of external vibrations make it challenging to obtain real-time reliable attitude measurement. To reduce the influence of attitude errors on test results, this paper proposes a Quaternion Nonlinear Complementary Filter (QNCF) attitude determination algorithm based on Microelectromechanical Inertial Measurement Unit (MEMS-IMU). Firstly, the threshold-based PI control strategy is adopted to eliminate noise effect according to the Acceleration Magnitude Detector (AMD). Then, the flexible quaternion method is updated to carry out attitude estimation which is operational and easy to be embedded in the Field Programmable Gate Array (FPGA). Finally, a high-precision three-axis turntable test and a hypersonic wind tunnel test are performed. The results show that the pitch-roll attitude errors are within 0.05° and 0.08° in the high-precision three-axis turntable test in a calculation time of 100 s respectively, and the attitude error is within 0.3° after the elastic angle correction in the hypersonic wind tunnel test. The proposed method can provide accurate real-time attitude reference for the analysis of the actual movement of the model, exhibiting certain engineering application value with robustness and simplicity.

Improved Pedestrian Dead Reckoning Based on a Robust Adaptive Kalman Filter for Indoor Inertial Location System.

Pedestrian dead reckoning (PDR) systems based on a microelectromechanical-inertial measurement unit (MEMS-IMU) providing advantages of full autonomy and strong anti-jamming performance are becoming a feasible choice for pedestrian indoor positioning. In order to realize the accurate positioning of pedestrians in a closed environment, an improved pedestrian dead reckoning algorithm, mainly including improved step estimation and heading estimation, is proposed in this paper. Firstly, the original signal is preprocessed using the wavelet denoising algorithm. Then, the multi-threshold method is proposed to ameliorate the step estimation algorithm. For heading estimation suffering from accumulated error and outliers, robust adaptive Kalman filter (RAKF) algorithm is proposed in this paper, and combined with complementary filter to improve positioning accuracy. Finally, an experimental platform with inertial sensors as the core is constructed. Experimental results show that positioning error is less than 2.5% of the total distance, which is ideal for accurate positioning of pedestrians in enclosed environment.

Sprint diagnostic with GPS and inertial sensor fusion

The purpose of this research was to develop a wearable, low-cost prototype based on real-time kinematic GPS and a microelectromechanical inertial measurement unit to measure the sprinting velocity of an athlete. The software package RTKLIB was used to calculate the RTK-GPS positions and different Kalman filters were implemented to provide a loosely coupled sensor fusion. With this setup, we performed empirical studies to determine whether the velocities obtained by this novel approach are sufficiently accurate for a performance orientated training. Therefore, field tests for 30- to 400-m sprint distance were conducted with simultaneous measurements with different reference systems, such as a laser device or timing gates. The evaluation revealed a correspondence between prototype and reference systems with distance and timing errors of $$\pm \, 2\,\%$$ and high correlations for the velocities (R = 0.996, P <0.001) for 68 % of the trials. However, for remaining 32 % of the trials no acceptable performance parameters could be obtained due to GPS problems. Overall, the developed prototype showed great potential and might allow closing the gap between the accuracy and flexibility of the established reference systems as soon as its susceptibility to GPS problems is lowered.

Improved pitch-constrained ambiguity function method for integer ambiguity resolution in BDS/MIMU-integrated attitude determination

The initialization is a critical step in the BeiDou Navigation Satellite System and microelectromechanical inertial measurement unit (MIMU)-integrated attitude determination. One of the primary tasks in the initializing process is integer ambiguity resolution. The rapidity and reliability of integer ambiguity resolution play a particularly important role in kinematic attitude determination. However, the common ambiguity function method (AFM) is time consuming and unreliable due to searching over the entire yaw and pitch range. In view of the fact that MIMU can provide initial pitch by self-alignment with the three-axis accelerometers, we present a pitch-constrained AFM (PCAFM) for single-epoch and single-frequency integer ambiguity resolution. Although only pitch information is available, both pitch and yaw search spaces can be constrained in PCAFM. The pitch search space is constrained by the initial pitch from MIMU, and then the yaw search candidates are reduced by the constrained pitch range with the mathematical relationship between yaw and pitch in the DD carrier-phase observation equation. Experimental results demonstrate that the yaw and pitch search candidates of PCAFM are greatly decreased by 67.55% and 97.51%, respectively. Meanwhile, the success rate of integer ambiguity resolution is improved compared with the AFM.

Performance analysis of an IMU-augmented GNSS tracking system on board the MAIUS-1 sounding rocket

Satellite navigation receivers are adequate tracking sensors for range safety of both orbital launch vehicles and suborbital sounding rockets. Due to high accuracy and its low system complexity, satellite navigation is seen as well-suited supplement or replacement of conventional tracking systems like radar. Having the well-known shortcomings of satellite navigation like deliberate or unintentional interferences in mind, it is proposed to augment the satellite navigation receiver by an inertial measurement unit (IMU) to enhance continuity and availability of localization. The augmented receiver is thus enabled to output at least an inertial position solution in case of signal outages. In a previous study, it was shown by means of simulation using the example of Ariane 5 that the performance of a low-grade microelectromechanical IMU is sufficient to bridge expected outages of some ten seconds, and still meeting the range safety requirements in effect. In this publication, these theoretical findings shall be substantiated by real flight data that were recorded on MAIUS-1, a sounding rocket launched from Esrange, Sweden, in early 2017. The analysis reveals that the chosen representative of a microelectromechanical IMU is suitable to bridge outages of up to thirty seconds.

An Optimal Calibration Method for a MEMS Inertial Measurement Unit

An optimal calibration method for a micro-electro-mechanical inertial measurement unit (MIMU) is presented in this paper. The accuracy of the MIMU is highly dependent on calibration to remove the deterministic errors of systematic errors, which also contain random errors. The overlapping Allan variance is applied to characterize the types of random error terms in the measurements. The calibration model includes package misalignment error, sensor-to-sensor misalignment error and bias, and a scale factor is built. The new concept of a calibration method, which includes a calibration scheme and a calibration algorithm, is proposed. The calibration scheme is designed by D-optimal and the calibration algorithm is deduced by a Kalman filter. In addition, the thermal calibration is investigated, as the bias and scale factor varied with temperature. The simulations and real tests verify the effectiveness of the proposed calibration method and show that it is better than the traditional method.

Time Synchronization Error and Calibration in Integrated GPS/INS Systems

The necessity for the precise time synchronization of measurement data from multiple sensors is widely recognized in the field of global positioning system/inertial navigation system (GPS/INS) integration. Having precise time synchronization is critical for achieving high data fusion performance. The limitations and advantages of various time synchronization scenarios and existing solutions are investigated in this paper. A criterion for evaluating synchronization accuracy requirements is derived on the basis of a comparison of the Kalman filter innovation series and the platform dynamics. An innovative time synchronization solution using a counter and two latching registers is proposed. The proposed solution has been implemented with off-the-shelf components and tested. The resolution and accuracy analysis shows that the proposed solution can achieve a time synchronization accuracy of 0.1 ms if INS can provide a hard-wired timing signal. A synchronization accuracy of 2 ms was achieved when the test system was used to synchronize a low-grade micro-electromechanical inertial measurement unit (IMU), which has only an RS-232 data output interface.

Pedestrian tracking and navigation using an adaptive knowledge system based on neural networks

The primary objective of the research presented here is to develop theoretical foundations and implementation algorithms, which integrate the Global Positioning System (GPS), micro-electromechanical inertial measurement unit (MEMS IMU), digital barometer, electronic compass, and human pedometry to provide navigation and tracking of military and rescue ground personnel. This paper discusses the design, implementation and the performance analyses of the personal navigator prototype, with a special emphasis on dead-reckoning (DR) navigation supported by the human locomotion model. The adaptive knowledge system, based on the Artificial Neural Networks (ANN), is implemented to support this functionality. The knowledge system is trained during the GPS signal reception and is used to support navigation under GPS-denied conditions. The human locomotion parameters, step frequency (SF) and step length (SL), are extracted from GPS-timed impact switches (step frequency) and GPS/IMU data (step length), respectively, during the system calibration period. SL is correlated with several data types, such as acceleration, acceleration variation, SF, terrain slope, etc. that constitute the input parameters to the ANN-based knowledge system. The ANN-predicted SL, together with the heading information from the compass and gyro, support DR navigation. The current target accuracy of the system is 3–5 m CEP (circular error probable) 50%.

Production Line Calibration for Sensors on Actively Controlled Bullets

A simple testing device is presented that simultaneously calibrates all dominant error sources for accelerometers and gyroscopes found in typical microelectromechanical inertial measurement units on smart projectiles, including bias, scale factor, cross axis sensitivity/misalignment, and misposition. The device consists of a table which rotates about a gimbal joint and is supported on the corners by elastic and damping elements. Using dynamic simulation it is shown that motion created by free vibration of the testing platform suitably excites sensors on projectiles so calibration can be performed. Calibration parameters are estimated using an extended Kalman filter. Platform support stiffness and damping characteristics significantly alter the time required to identify calibration parameters by shaping platform motion. A critical level of initial motion of the testing platform is required for adequate prediction of calibration parameters.