scholarly journals A Rigorous Temperature-Dependent Stochastic Modelling and Testing for MEMS-Based Inertial Sensor Errors

Sensors ◽  
2009 ◽  
Vol 9 (11) ◽  
pp. 8473-8489 ◽  
Author(s):  
Mohammed El-Diasty ◽  
Spiros Pagiatakis
Keyword(s):  
Inertial Sensor ◽  
Stochastic Modelling ◽  
Temperature Dependent ◽  
Sensor Errors

scholarly journals A Rapid Transfer Alignment Method for SINS Based on the Added Backward-Forward SINS Resolution and Data Fusion

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Xixiang Liu ◽  
Xiaosu Xu ◽  
Yiting Liu ◽  
Lihui Wang
Keyword(s):  
Data Fusion ◽  
Reference Data ◽  
Inertial Sensor ◽  
Sensor Data ◽  
Alignment Method ◽  
Initial Alignment ◽  
Transfer Alignment ◽  
Gyro Bias ◽  
Sensor Errors ◽  
Rapid Transfer

Two viewpoints are given: (1) initial alignment of strapdown inertial navigation system (SINS) can be fulfilled with a set of inertial sensor data; (2) estimation time for sensor errors can be shortened by repeated data fusion on the added backward-forward SINS resolution results and the external reference data. Based on the above viewpoints, aiming to estimate gyro bias in a shortened time, a rapid transfer alignment method, without any changes for Kalman filter, is introduced. In this method, inertial sensor data and reference data in one reference data update cycle are stored, and one backward and one forward SINS resolutions are executed. Meanwhile, data fusion is executed when the corresponding resolution ends. With the added backward-forward SINS resolution, in the above mentioned update cycle, the estimating operations for gyro bias are added twice, and the estimation time for it is shortened. In the ship swinging condition, with the “velocity plus yaw” matching, the effectiveness of this method is proved by the simulation.

scholarly journals Comparing Clothing-Mounted Sensors with Wearable Sensors for Movement Analysis and Activity Classification

Sensors ◽  
2019 ◽  
Vol 20 (1) ◽  
pp. 82 ◽  
Author(s):  
Udeni Jayasinghe ◽  
William S. Harwin ◽  
Faustina Hwang
Keyword(s):  
Inertial Sensors ◽  
Inertial Sensor ◽  
Wearable Sensors ◽  
Correlation Coefficients ◽  
Movement Analysis ◽  
Multiple Sensors ◽  
Sensor Orientation ◽  
Multiple Devices ◽  
Good Potential ◽  
Sensor Errors

Inertial sensors are a useful instrument for long term monitoring in healthcare. In many cases, inertial sensor devices can be worn as an accessory or integrated into smart textiles. In some situations, it may be beneficial to have data from multiple inertial sensors, rather than relying on a single worn sensor, since this may increase the accuracy of the analysis and better tolerate sensor errors. Integrating multiple sensors into clothing improves the feasibility and practicality of wearing multiple devices every day, in approximately the same location, with less likelihood of incorrect sensor orientation. To facilitate this, the current work investigates the consequences of attaching lightweight sensors to loose clothes. The intention of this paper is to discuss how data from these clothing sensors compare with similarly placed body worn sensors, with additional consideration of the resulting effects on activity recognition. This study compares the similarity between the two signals (body worn and clothing), collected from three different clothing types (slacks, pencil skirt and loose frock), across multiple daily activities (walking, running, sitting, and riding a bus) by calculating correlation coefficients for each sensor pair. Even though the two data streams are clearly different from each other, the results indicate that there is good potential of achieving high classification accuracy when using inertial sensors in clothing.

scholarly journals A regression‐based methodology to improve estimation of inertial sensor errors using Allan variance data

Navigation ◽  
2019 ◽  
Vol 66 (1) ◽  
pp. 251-263 ◽  
Author(s):  
Juan Jurado ◽  
Christine M. Schubert Kabban ◽  
John Raquet
Keyword(s):  
Inertial Sensor ◽  
Allan Variance ◽  
Sensor Errors

Methods for Improving Foot Displacement Measurements Calculated from Inertial Sensors

2011 ◽  
pp. 93-116 ◽  
Author(s):  
Edgar Charry ◽  
Daniel T.H. Lai
Keyword(s):  
Measurement System ◽  
Inertial Sensors ◽  
Inertial Sensor ◽  
Human Movement ◽  
Sensor Data ◽  
Band Pass Filter ◽  
Pass Filter ◽  
Band Pass ◽  
Sensor Errors ◽  
Displacement Measurements

The use of inertial sensors to measure human movement has recently gained momentum with the advent of low cost micro-electro-mechanical systems (MEMS) technology. These sensors comprise accelerometer and gyroscopes which measure accelerations and angular velocities respectively. Secondary quantities such as displacement can be obtained by integration of these quantities, a method which presents challenging issues due to the problem of accumulative sensor errors. This chapter investigates the spectral evaluation of individual sensor errors and looks at the effectiveness of minimizing these errors using static digital filters. The primary focus is on the derivation of foot displacement data from inertial sensor measurements. The importance of foot, in particular toe displacement measurements is evident in the context of tripping and falling which are serious health concerns for the elderly. The Minimum Toe Clearance (MTC) as an important gait variable for falls-risk prediction and assessment, and therefore the measurement variable of interest. A brief sketch of the current devices employing accelerometers and gyroscopes is presented, highlighting the problems and difficulties reported in literature to achieve good precision. These have been mainly due to the presence of sensor errors and the error accumulative process employed in obtaining displacement measurements. The investigation first proceeds to identify the location of these sensor errors in the frequency domain using the Fast Fourier Transform (FFT) on raw inertial sensor data. The frequency content of velocity and displacement measurements obtained from integrating the inertial data using a well known strap-down method is then explored. These investigations revealed that large sensor errors occurred mainly in the low frequency spectrum while white noise exists in all frequency spectra. The efficacy of employing a band-pass filter to remove a large portion of these errors and their effect on the derived displacements is elaborated on. The cross-correlation of the FFT power spectra from a highly accurate optical measurement system and processed sensor data is used as a metric to evaluate the performance of the band-pass filter at several stages of the processing stage. The motivation is that a more fundamental method would require less computational demand and could lead to more efficient implementations in low-power and systems with limited resources, so that portable sensor based motion measurement system would provide a good degree of measurement accuracy.

Errors in micro-electro-mechanical systems inertial measurement and a review on present practices of error modelling

2017 ◽  
Vol 40 (9) ◽  
pp. 2843-2854 ◽  
Author(s):  
Renu Bhardwaj ◽  
Neelesh Kumar ◽  
Vipan Kumar
Keyword(s):  
Inertial Sensors ◽  
Inertial Sensor ◽  
Mechanical Systems ◽  
Stochastic Modelling ◽  
Future Research ◽  
Mems Devices ◽  
Specific System ◽  
Micro Electro Mechanical Systems ◽  
Inertial Measurement ◽  
Calibration Methods

Micro-electro-mechanical systems (MEMS) technology-based accelerometers and gyroscopes are small size, mass produced, low cost inertial sensors, which are now being used in aerospace, underwater vehicles, automotive, robotics, mobiles, gaming consoles, prosthetic devices and many other applications. MEMS inertial sensors are available in many grades in market and selecting the appropriate grade sensor is very important. Owing to interaction of different types of energies, different noises are generated in MEMS devices; these noises cause significant change in output and the first section of this paper illustrates that. In application, where MEMS inertial sensors are used, the accuracy, repeatability and reproducibility of inertia measurement is probed primarily by complex testing, using extensive range of physical stimuli. Noises in inertial measurement are generally dealt by designing a unit measurement model. Noises are treated as additive error in linear unit model and are modelled using various techniques so that errors can be compensated to improve the accuracy. This paper reviews the theory, framework and methodology used in the error model of a MEMS inertial sensor and stochastic modelling of measurement. Experimental results from the most commonly used Allan variance techniques are discussed. Error modelling methodology, consisting of testing and calibration methods, designing thermal model, stochastic modelling and parameter estimation techniques, is illustrated. Figures and tables under each section summarize features, merits, limitation and future research scope. This paper should serve as a single reference for researchers and engineers working on application specific system design and instrumentation using MEMS inertial sensors. Conclusion from the study should help in selecting the appropriate grade of sensor as well as the best error modelling as per the trade-off existing between accuracy and development cost of error modelling.

scholarly journals Robust Modeling of Low-Cost MEMS Sensor Errors in Mobile Devices Using Fast Orthogonal Search

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
M. Tamazin ◽  
A. Noureldin ◽  
M. J. Korenberg
Keyword(s):  
Markov Model ◽  
Mobile Devices ◽  
Inertial Sensor ◽  
Low Cost ◽  
Location Based Services ◽  
Robust Modeling ◽  
First Order ◽  
Orthogonal Search ◽  
Fast Orthogonal Search ◽  
Sensor Errors

Accessibility to inertial navigation systems (INS) has been severely limited by cost in the past. The introduction of low-cost microelectromechanical system-based INS to be integrated with GPS in order to provide a reliable positioning solution has provided more wide spread use in mobile devices. The random errors of the MEMS inertial sensors may deteriorate the overall system accuracy in mobile devices. These errors are modeled stochastically and are included in the error model of the estimated techniques used such as Kalman filter or Particle filter. First-order Gauss-Markov model is usually used to describe the stochastic nature of these errors. However, if the autocorrelation sequences of these random components are examined, it can be determined that first-order Gauss-Markov model is not adequate to describe such stochastic behavior. A robust modeling technique based on fast orthogonal search is introduced to remove MEMS-based inertial sensor errors inside mobile devices that are used for several location-based services. The proposed method is applied to MEMS-based gyroscopes and accelerometers. Results show that the proposed method models low-cost MEMS sensors errors with no need for denoising techniques and using smaller model order and less computation, outperforming traditional methods by two orders of magnitude.

scholarly journals Tuning of the Wavelet Filters for the IMU Data Based on the PDC Method and the GPS Solution in a Bi-Dimensional Navigation Application

2019 ◽  
Vol 11 (3) ◽  
pp. 3-14
Author(s):  
Ioana Raluca ADOCHIEI ◽  
Teodor Lucian GRIGORIE ◽  
Felix Constantin ADOCHIEI
Keyword(s):  
Inertial Sensors ◽  
Inertial Sensor ◽  
Navigation Systems ◽  
Integrated Navigation ◽  
Wavelet Filters ◽  
Time Frequency ◽  
Tuning Method ◽  
Coupling Dynamics ◽  
Navigation Accuracy ◽  
Sensor Errors

The degradation of navigation accuracy and integrity of GPS in the presence of radio frequency interference, hostile jamming and high dynamical situations, when the satellite signals may get lost due to signal blockage, led to the development of MEMS-INS/GPS integrated navigation systems for various applications of the positioning and navigation technologies. Unfortunately, the short-term advantages brought by the INS systems are overshadowed by their imprecise operation over the long term, mainly due to inertial sensor errors. A critical component of the inertial sensors errors is the noise. To improve the quality of the inertial sensors data, many denoising techniques have been used. Wavelet method has been proven as a useful tool for signal analysis, and it is widely used in signal processing and denoising applications. The here proposed technique is based on a time-frequency approach previously applied in bio-signals processing. In the proposed mechanism, the inertial sensors signals are processed analysed by using an extended version of the Wavelet transform. The optimal levels of decomposition are established for the wavelet filters, based on the evaluation of a parameter called coupling level (CL). It characterizes the coupling dynamics information between the reference signals, provided by a GPS, and the perturbed signal, which are the outputs of the inertial navigation system (INS). The proposed tuning method is experimentally tested in a bi-dimensional navigation application.

Global Positioning System (GPS): A Low-Cost Velocity Sensor for Correcting Inertial Sensor Errors on Ground Vehicles

2004 ◽  
Vol 126 (2) ◽  
pp. 255-264 ◽  
Author(s):  
David M. Bevly
Keyword(s):  
Kalman Filter ◽  
Global Positioning System ◽  
Inertial Sensors ◽  
Inertial Sensor ◽  
Low Cost ◽  
Positioning System ◽  
Ground Vehicles ◽  
Velocity Sensor ◽  
Global Positioning ◽  
Sensor Errors

This paper demonstrates the ability of a standard low-cost Global Positioning System (GPS) receiver to reduce errors inherent in low-cost accelerometers and rate gyroscopes used on ground vehicles. Specifically GPS velocity is used to obtain vehicle course, velocity, and road grade, as well as to correct inertial sensors errors, providing accurate longitudinal and lateral acceleration, and pitch, roll, and yaw angular velocities. Additionally, it is shown that transient changes in sideslip (or lateral velocity), roll, and pitch angles can be measured. The method utilizes GPS velocity measurements to determine the inertial sensor errors using a kinematic Kalman Filter estimator. Simple models of the inertial sensors, which take into account the sensor noise and bias drift properties, are developed and used to design the estimator. Based on the characteristics of low-cost GPS receivers and IMU sensors, this paper presents the achievable performance of the combined system using the covariance analysis from the Kalman filter. Subsequent simulations and experiments validate both the error analysis and the methodology for utilizing GPS as a velocity sensor for correcting low-cost inertial sensor errors and providing critical vehicle state measurements.

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