scholarly journals Performance Enhancement of Consumer-Grade MEMS Sensors through Geometrical Redundancy

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4851
Author(s):  
Giorgio de Alteriis ◽  
Domenico Accardo ◽  
Claudia Conte ◽  
Rosario Schiano Lo Moriello
Keyword(s):  
Random Walk ◽  
Performance Enhancement ◽  
Inertial Sensors ◽  
Low Cost ◽  
Cost Effective ◽  
Measurement Procedure ◽  
Measurement Unit ◽  
Mems Sensors ◽  
Mems Technology ◽  
Bias Instability

The paper deals with performance enhancement of low-cost, consumer-grade inertial sensors realized by means of Micro Electro-Mechanical Systems (MEMS) technology. Focusing their attention on the reduction of bias instability and random walk-driven drift of cost-effective MEMS accelerometers and gyroscopes, the authors hereinafter propose a suitable method, based on a redundant configuration and complemented with a proper measurement procedure, to improve the performance of low-cost, consumer-grade MEMS sensors. The performance of the method is assessed by means of an adequate prototype and compared with that assured by a commercial, expensive, tactical-grade MEMS inertial measurement unit, taken as reference. Obtained results highlight the promising reliability and efficacy of the method in estimating position, velocity, and attitude of vehicles; in particular, bias instability and random walk reduction greater than 25% is, in fact, experienced. Moreover, differences as low as 0.025 rad and 0.89 m are obtained when comparing position and attitude estimates provided by the prototype and those granted by the tactical-grade MEMS IMU.

A Standard Testing and Calibration Procedure for Low Cost MEMS Inertial Sensors and Units

2008 ◽  
Vol 61 (2) ◽  
pp. 323-336 ◽  
Author(s):  
P. Aggarwal ◽  
Z. Syed ◽  
X. Niu ◽  
N. El-Sheimy
Keyword(s):  
Moving Objects ◽  
Inertial Sensors ◽  
Low Cost ◽  
Calibration Method ◽  
Calibration Procedure ◽  
Measurement Unit ◽  
Navigation Systems ◽  
Integrated Navigation ◽  
Mems Sensors ◽  
Calibration Methods

Navigation involves the integration of methodologies and systems for estimating the time varying position and attitude of moving objects. Inertial Navigation Systems (INS) and the Global Positioning System (GPS) are among the most widely used navigation systems. The use of cost effective MEMS based inertial sensors has made GPS/INS integrated navigation systems more affordable. However MEMS sensors suffer from various errors that have to be calibrated and compensated to get acceptable navigation results. Moreover the performance characteristics of these sensors are highly dependent on the environmental conditions such as temperature variations. Hence there is a need for the development of accurate, reliable and efficient thermal models to reduce the effect of these errors that can potentially degrade the system performance. In this paper, the Allan variance method is used to characterize the noise in the MEMS sensors. A six-position calibration method is applied to estimate the deterministic sensor errors such as bias, scale factor, and non-orthogonality. An efficient thermal variation model is proposed and the effectiveness of the proposed calibration methods is investigated through a kinematic van test using integrated GPS and MEMS-based inertial measurement unit (IMU).

scholarly journals Feedforward ins aiding: an investigation of maneuvers for in-flight alignment

2007 ◽  
Vol 18 (4) ◽  
pp. 459-470 ◽  
Author(s):  
Jacques Waldmann
Keyword(s):  
Autonomous Vehicles ◽  
Inertial Sensors ◽  
Low Cost ◽  
Measurement Unit ◽  
Model Mismatch ◽  
External Data ◽  
Rate Gyro ◽  
External Position ◽  
The Impact ◽  
Gyro Drift

Navigation in autonomous vehicles involves integrating measurements from on-board inertial sensors and external data collected by various sensors. In this paper, the computer-frame velocity error model is augmented with a random constant model of accelerometer bias and rate-gyro drift for use in a Kalman filter-based fusion of a low-cost rotating inertial navigation system (INS) with external position and velocity measurements. The impact of model mismatch and maneuvers on the estimation of misalignment and inertial measurement unit (IMU) error is investigated. Previously, the literature focused on analyzing the stripped observability matrix that results from applying piece-wise constant acceleration segments to a stabilized, gimbaled INS to determine the accuracy of misalignment, accelerometer bias, and rate-gyro drift estimation. However, its validation via covariance analysis neglected model mismatch. Here, a vertically undamped, three channel INS with a rotating IMU with respect to the host vehicle is simulated. Such IMU rotation does not require the accurate mechanism of a gimbaled INS (GINS) and obviates the need to maneuver away from the desired trajectory during in-flight alignment (IFA) with a strapdown IMU. In comparison with a stationary GINS at a known location, IMU rotation enhances estimation of accelerometer bias, and partially improves estimation of rate-gyro drift and misalignment. Finally, combining IMU rotation with distinct acceleration segments yields full observability, thus significantly enhancing estimation of rate-gyro drift and misalignment.

scholarly journals Adaptive Filtering on GPS-Aided MEMS-IMU for Optimal Estimation of Ground Vehicle Trajectory

Sensors ◽  
2019 ◽  
Vol 19 (24) ◽  
pp. 5357 ◽  
Author(s):  
Haseeb Ahmed ◽  
Ihsan Ullah ◽  
Uzair Khan ◽  
Muhammad Bilal Qureshi ◽  
Sajjad Manzoor ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Autonomous Navigation ◽  
Inertial Sensors ◽  
Low Cost ◽  
Optimal Estimation ◽  
Cost Effective ◽  
Estimation Accuracy ◽  
Ground Vehicle ◽  
Navigation Data ◽  
Mems Imu

Fusion of the Global Positioning System (GPS) and Inertial Navigation System (INS) for navigation of ground vehicles is an extensively researched topic for military and civilian applications. Micro-electro-mechanical-systems-based inertial measurement units (MEMS-IMU) are being widely used in numerous commercial applications due to their low cost; however, they are characterized by relatively poor accuracy when compared with more expensive counterparts. With a sudden boom in research and development of autonomous navigation technology for consumer vehicles, the need to enhance estimation accuracy and reliability has become critical, while aiming to deliver a cost-effective solution. Optimal fusion of commercially available, low-cost MEMS-IMU and the GPS may provide one such solution. Different variants of the Kalman filter have been proposed and implemented for integration of the GPS and the INS. This paper proposes a framework for the fusion of adaptive Kalman filters, based on Sage-Husa and strong tracking filtering algorithms, implemented on MEMS-IMU and the GPS for the case of a ground vehicle. The error models of the inertial sensors have also been implemented to achieve reliable and accurate estimations. Simulations have been carried out on actual navigation data from a test vehicle. Measurements were obtained using commercially available GPS receiver and MEMS-IMU. The solution was shown to enhance navigation accuracy when compared to conventional Kalman filter.

The Correcting Approach of Gyroscope-Free Inertial Navigation Based on the Applicable Topological Map

2019 ◽  
Vol 19 (2) ◽  
Author(s):  
Mehdi Dehghani ◽  
Hamed Kharrati ◽  
Hadi Seyedarabi ◽  
Mahdi Baradarannia
Keyword(s):  
Mobile Robot ◽  
Mobile Robots ◽  
Inertial Sensors ◽  
Low Cost ◽  
Measurement Unit ◽  
Sensor Calibration ◽  
Topological Map ◽  
Stereo Cameras ◽  
Long Time ◽  
Navigation Errors

The accumulated error and noise sensitivity are the two common problems of ordinary inertial sensors. An accurate gyroscope is too expensive, which is not normally applicable in low-cost missions of mobile robots. Since the accelerometers are rather cheaper than similar types of gyroscopes, using redundant accelerometers could be considered as an alternative. This mechanism is called gyroscope-free navigation. The article deals with autonomous mobile robot (AMR) navigation based on gyroscope-free method. In this research, the navigation errors of the gyroscope-free method in long-time missions are demonstrated. To compensate the position error, the aid information of low-cost stereo cameras and a topological map of the workspace are employed in the navigation system. After precise sensor calibration, an amendment algorithm is presented to fuse the measurement of gyroscope-free inertial measurement unit (GFIMU) and stereo camera observations. The advantages and comparisons of vision aid navigation and gyroscope-free navigation of mobile robots will be also discussed. The experimental results show the increasing accuracy in vision-aid navigation of mobile robot.

scholarly journals Feasibility Study of Multi Inertial Measurement Unit

Proceedings ◽  
2019 ◽  
Vol 42 (1) ◽  
pp. 74 ◽  
Author(s):  
Ariel Larey ◽  
Eliel Aknin ◽  
Itzik Klein
Keyword(s):  
Inertial Measurement Unit ◽  
Inertial Sensors ◽  
Low Cost ◽  
Software Tool ◽  
Experimental Results ◽  
Measurement Unit ◽  
Outlier Rejection ◽  
Inertial Measurement ◽  
Navigation Solution ◽  
Sensory Data

An inertial measurement unit (IMU) typically has three accelerometers and three gyroscopes. The output of those inertial sensors is used by an inertial navigation system to calculate the navigation solution–position, velocity and attitude. Since the sensor measurements contain noise, the navigation solution drifts over time. When considering low cost sensors, multiple IMUs can be used to improve the performance of a single unit. In this paper, we describe our designed 32 multi-IMU (MIMU) architecture and present experimental results using this system. To analyze the sensory data, a dedicated software tool, capable of addressing MIMUs inputs, was developed. Using the MIMU hardware and software tool we examined and evaluated the MIMUs for: (1) navigation solution accuracy (2) sensor outlier rejection (3) stationary calibration performance (4) coarse alignment accuracy and (5) the effect of different MIMUs locations in the architecture. Our experimental results show that 32 IMUs obtained better performance than a single IMU for all testcases examined. In addition, we show that performance was improved gradually as the number of IMUs was increased in the architecture.

scholarly journals Assessment of Breathing Parameters Using an Inertial Measurement Unit (IMU)-Based System

Sensors ◽  
2018 ◽  
Vol 19 (1) ◽  
pp. 88 ◽  
Author(s):  
Ambra Cesareo ◽  
Ylenia Previtali ◽  
Emilia Biffi ◽  
Andrea Aliverti
Keyword(s):  
Inertial Sensors ◽  
Low Cost ◽  
Estimation Error ◽  
Processing Algorithm ◽  
Measurement Unit ◽  
Breathing Patterns ◽  
Independent Manner ◽  
Inertial Measurement ◽  
Optoelectronic Plethysmography ◽  
The One

Breathing frequency (fB) is an important vital sign that—if appropriately monitored—may help to predict clinical adverse events. Inertial sensors open the door to the development of low-cost, wearable, and easy-to-use breathing-monitoring systems. The present paper proposes a new posture-independent processing algorithm for breath-by-breath extraction of breathing temporal parameters from chest-wall inclination change signals measured using inertial measurement units. An important step of the processing algorithm is dimension reduction (DR) that allows the extraction of a single respiratory signal starting from 4-component quaternion data. Three different DR methods are proposed and compared in terms of accuracy of breathing temporal parameter estimation, in a group of healthy subjects, considering different breathing patterns and different postures; optoelectronic plethysmography was used as reference system. In this study, we found that the method based on PCA-fusion of the four quaternion components provided the best fB estimation performance in terms of mean absolute errors (<2 breaths/min), correlation (r > 0.963) and Bland–Altman Analysis, outperforming the other two methods, based on the selection of a single quaternion component, identified on the basis of spectral analysis; particularly, in supine position, results provided by PCA-based method were even better than those obtained with the ideal quaternion component, determined a posteriori as the one providing the minimum estimation error. The proposed algorithm and system were able to successfully reconstruct the respiration-induced movement, and to accurately determine the respiratory rate in an automatic, position-independent manner.

scholarly journals Design and Implementation of an AHRS Based on MEMS Sensors and Complementary Filtering

2014 ◽  
Vol 6 ◽  
pp. 214726 ◽  
Author(s):  
Yangzhu Wang ◽  
Ning Li ◽  
Xi Chen ◽  
Miao Liu
Keyword(s):  
Low Cost ◽  
Low Frequency ◽  
Measurement Unit ◽  
Accurate Estimation ◽  
Extended Kalman Filtering ◽  
Mems Sensors ◽  
Features Fusion ◽  
Design And Implementation ◽  
Mems Imu ◽  
Experimental Comparisons

This paper presents design and implementation of an attitude and heading reference system (AHRS) based on low-cost MEMS sensors and complementary filtering (CF). Different from traditional solutions, information fusion is performed with Euler angles directly, which is more straightforward for understanding; however it proposes many challenges for reaching a stable and accurate estimation as when these angles approach or traverse their range boundaries, estimation may get discontinuous. Thus an effective discontinuity avoiding strategy is suggested in this paper to refine the estimation. Besides, instead of extended Kalman filtering (EKF), CF is utilized for state estimation of AHRS as it features fusion of high-frequency and low-frequency signals. In order to make up for shortcomings of MEMS sensors such as multiple errors, drifts, and bad accuracy, some effective calibration and filtering algorithms are proposed to guarantee agreeable AHRS performance. Also, architecture of the MEMS IMU (inertial measurement unit) and mathematical principles for AHRS solution are explained and implemented in this paper. Meanwhile, experimental comparisons have proved feasibility and acceptable performance of this AHRS design.

scholarly journals Performance Characterization of GNSS/IMU/DVL Integration under Real Maritime Jamming Conditions

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2954 ◽  
Author(s):  
Ralf Ziebold ◽  
Daniel Medina ◽  
Michailas Romanovas ◽  
Christoph Lass ◽  
Stefan Gewies
Keyword(s):  
Inertial Sensors ◽  
Unscented Kalman Filter ◽  
Low Cost ◽  
Primary Source ◽  
Global Navigation Satellite Systems ◽  
Measurement Unit ◽  
Doppler Velocity ◽  
Satellite Systems ◽  
Tightly Coupled ◽  
The Baltic

Currently Global Navigation Satellite Systems (GNSSs) are the primary source for the determination of absolute position, navigation, and time (PNT) for merchant vessel navigation. Nevertheless, the performance of GNSSs can strongly degrade due to space weather events, jamming, and spoofing. Especially the increasing availability and adoption of low cost jammers lead to the question of how a continuous provision of PNT data can be realized in the vicinity of these devices. In general, three possible solutions for that challenge can be seen: (i) a jamming-resistant GNSS receiver; (ii) the usage of a terrestrial backup system; or (iii) the integration of GNSS with other onboard navigation sensors such as a speed log, a gyrocompass, and inertial sensors (inertial measurement unit—IMU). The present paper focuses on the third option by augmenting a classical IMU/GNSS sensor fusion scheme with a Doppler velocity log. Although the benefits of integrated IMU/GNSS navigation system have been already demonstrated for marine applications, a performance evaluation of such a multi-sensor system under real jamming conditions on a vessel seems to be still missing. The paper evaluates both loosely and tightly coupled fusion strategies implemented using an unscented Kalman filter (UKF). The performance of the proposed scheme is evaluated using the civilian maritime jamming testbed in the Baltic Sea.

scholarly journals Analysis and Verification of Rotation Modulation Effects on Inertial Navigation System based on MEMS Sensors

2013 ◽  
Vol 66 (5) ◽  
pp. 751-772 ◽  
Author(s):  
Xueyun Wang ◽  
Jie Wu ◽  
Tao Xu ◽  
Wei Wang
Keyword(s):  
Navigation System ◽  
Inertial Navigation System ◽  
Inertial Sensors ◽  
Inertial Navigation ◽  
Cost Effective ◽  
Navigation Systems ◽  
Inertial Navigation Systems ◽  
Mems Sensors ◽  
Micro Electro Mechanical Systems ◽  
Position Accuracy

Inertial Navigation Systems (INS) were large, heavy and expensive until the development of cost-effective inertial sensors constructed with Micro-electro-mechanical systems (MEMS). However, the large errors and poor error repeatability of MEMS sensors make them inadequate for application in many situations even with frequent calibration. To solve this problem, a systematic error auto-compensation method, Rotation Modulation (RM) is introduced and detailed. RM does no damage to autonomy, which is one of the most important characteristics of an INS. In this paper, the RM effects on navigation performance are analysed and different forms of rotation schemes are discussed. A MEMS-based INS with the RM technique applied is developed and specific calibrations related to rotation are investigated. Experiments on the developed system are conducted and results verify that RM can significantly improve navigation performance of MEMS-based INS. The attitude accuracy is improved by a factor of 5, and velocity/position accuracy by a factor of 10.

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