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Accounting for Vibration Noise in Stochastic Measurement Errors of Inertial Sensors
Mucyo Karemera;Lionel Voirol;Davide A. Cucci;Wenfei Chu;Roberto Molinari;Stéphane Guerrier
The measurement of data over time and/or space is of utmost importance in a wide range of domains from engineering to physics. Devices that perform these measurements, such as inertial sensors, need to be extremely precise to obtain correct system diagnostics and accurate predictions, consequently requiring a rigorous calibration procedure before being employed. Most of the research over the past ...Show More
On Performance Evaluation of Inertial Navigation Systems: The Case of Stochastic Calibration
Davide A. Cucci;Lionel Voirol;Mehran Khaghani;Stéphane Guerrier
In this work, we address the problem of rigorously evaluating the performances of an inertial navigation system (INS) during its design phase in presence of multiple alternative choices. We introduce a framework based on Monte Carlo simulations in which a standard extended Kalman filter is coupled with realistic and user-configurable noise generation mechanisms to recover a reference trajectory fr...Show More
Multi-Signal Approaches for Repeated Sampling Schemes in Inertial Sensor Calibration
Gaetan Bakalli;Davide A. Cucci;Ahmed Radi;Naser El-Sheimy;Roberto Molinari;Olivier Scaillet;Stéphane Guerrier
Inertial sensor calibration plays a progressively important role in many areas of research among which navigation engineering. By performing this task accurately, it is possible to significantly increase general navigation performance by correctly filtering out the deterministic and stochastic measurement errors that characterize such devices. While different techniques are available to model and ...Show More
Scale-Wise Variance Minimization for Optimal Virtual Signals: An Approach for Redundant Gyroscopes
Yuming Zhang;Davide A. Cucci;Roberto Molinari;Stéphane Guerrier
The increased use of low-cost gyroscopes within inertial sensors for navigation purposes, among others, has brought to the development of a considerable amount of research in improving their measurement precision. An approach that has been put forward in recent years is to make use of arrays of such sensors to combine their measurements thereby reducing the impact of individual sensor noise. Never...Show More
Optimally Weighted Wavelet Variance-based Estimation for Inertial Sensor Stochastic Calibration
Lionel Voirol;Stéphane Guerrier;Yuming Zhang;Mucyo Karemera;Ahmed Radi
2020 12th International Conference on Electrical Engineering (ICEENG)
Year: 2020 | Conference Paper |
Cited by: Papers (1)
Different inertial sensor calibration techniques have been proposed to consider the sources of measurement error from inertial sensors. There has been a significant amount of literature which studies the stochastic errors calibration of such devices. The recent results of [1] have proved that among all possible methods the (Generalized Method of Wavelet Moments) (GMWM) presents various optimality ...Show More
Wavelet-Based Moment-Matching Techniques for Inertial Sensor Calibration
Stéphane Guerrier;Juan Jurado;Mehran Khaghani;Gaetan Bakalli;Mucyo Karemera;Roberto Molinari;Samuel Orso;John Raquet;Christine Schubert;Jan Skaloud;Haotian Xu;Yuming Zhang
The task of inertial sensor calibration has required the development of various techniques to take into account the sources of measurement error coming from such devices. The calibration of the stochastic errors of these sensors has been the focus of increasing amount of research in which the method of reference has been the so-called “Allan variance (AV) slope method” which, in addition to not ha...Show More
Multivariate Signal Modeling With Applications to Inertial Sensor Calibration
Haotian Xu;Stéphane Guerrier;Roberto Carlo Molinari;Mucyo Karemera
The common approach to inertial sensor calibration has been to model the stochastic error signals of individual sensors independently, whether as components of a single inertial measurement unit (IMU) in different directions or arrayed in the same direction for redundancy. For this purpose, research in this domain has been focused on the proposal of various methods to improve the estimation of the...Show More
A Multisignal Wavelet Variance-Based Framework for Inertial Sensor Stochastic Error Modeling
Ahmed Radi;Gaetan Bakalli;Stéphane Guerrier;Naser El-Sheimy;Abu B. Sesay;Roberto Molinari
The calibration of low-cost inertial sensors has become increasingly important over the last couple of decades, especially when dealing with sensor stochastic errors. This procedure is commonly performed on a single error measurement from an inertial sensor taken over a certain amount of time, although it is extremely frequent for different replicates to be taken for the same sensor, thereby deliv...Show More
Use of a new online calibration platform with applications to inertial sensors
Philipp Clausen;Jan Skaloud;Roberto Molinari;Justin Lee;Stephane Guerrier
In many fields, going from economics to physics, it is common to deal with measurements that are taken in time. These measurements are often explained by known external factors that describe a large part of their behavior. For example, the evolution of the unemployment rate in time can be explained by the behavior of the gross domestic product (the external factor in this case). However, in many c...Show More
A two-step computationally efficient procedure for IMU classification and calibration
Gaetan Bakalli;Ahmed Radi;Sameh Nassar;Stephane Guerrier;Yuming Zhang;Roberto Molinari
2018 IEEE/ION Position, Location and Navigation Symposium (PLANS)
Year: 2018 | Conference Paper |
Cited by: Papers (3)
The task of inertial sensor calibration has always been challenging, especially when dealing with stochastic errors that remain after the deterministic errors have been filtered out. Among others, the number of observations is becoming increasingly high since sensor measurements are taken at high frequencies over longer periods of time, thereby placing considerable limitations on the estimation of...Show More
Construction of dynamically-dependent stochastic error models
2018 IEEE/ION Position, Location and Navigation Symposium (PLANS)
Year: 2018 | Conference Paper |
Cited by: Papers (1)
Stochastic behavior of an instrument is often analyzed by constructing the Allan (or wavelet) variance signatures from an error signal. For inertial sensors, such a signature is conveniently obtained by recording data at rest. The analysis of this signal will result in noise-parameters adequate to such situation. Nonetheless, the value of the noise parameters may change under dynamics or other kin...Show More
Improved stochastic modelling of low-cost GNSS receivers positioning errors
Ahmed Radi;Sameh Nassar;Maan Khedr;Naser El-Sheimy;Roberto Molinari;Stéphane Guerrier
2018 IEEE/ION Position, Location and Navigation Symposium (PLANS)
Year: 2018 | Conference Paper |
Cited by: Papers (3)
The Global Navigation Satellite System (GNSS) is currently used in many fields, such as autonomous driving, robotics application, and Unmanned Aerial Vehicles (UAVs), where accurate position information is required. These applications require high positioning accuracy which, in turn, require precise analysis of the residual noise characteristics of the GNSS positioning solutions and their quantita...Show More
An optimal virtual inertial sensor framework using wavelet cross covariance
Yuming Zhang;Haotian Xu;Ahmed Radi;Roberto Molinari;Stéphane Guerrier;Mucyo Karemera;Naser El-Sheimy
2018 IEEE/ION Position, Location and Navigation Symposium (PLANS)
Year: 2018 | Conference Paper |
Cited by: Papers (2)
The practice of inertial sensor calibration has commonly been carried out by taking into account the deterministic and stochastic components of the error measurements issued from a calibration session. Once the deterministic components have been taken into account through physical models, the remaining stochastic component has always been dealt with for each sensor separately. The latter process i...Show More
A Computationally Efficient Framework for Automatic Inertial Sensor Calibration
James Joseph Balamuta;Roberto Molinari;Stéphane Guerrier;Wenchao Yang
The calibration of (low-cost) inertial sensors has become increasingly important over the past years, since their use has grown exponentially in many applications going from unmanned aerial vehicle navigation to 3-D animation. However, this calibration procedure is often quite problematic since, aside from compensating for deterministic measurement errors due to physical phenomena such as dynamics...Show More
An overview of a new sensor calibration platform
Philipp Clausen;Jan Skaloud;Roberto Molinari;James Balamuta;Stéphane Guerrier
2017 IEEE International Workshop on Metrology for AeroSpace (MetroAeroSpace)
Year: 2017 | Conference Paper |
Cited by: Papers (1)
Inertial sensors are increasingly being employed in different types of applications. The reduced cost and the extremely small size makes them the number-one-choice in miniature embedded devices like phones, watches, and small unmanned aerial vehicles. The more complex the application, the more it is necessary to understand the structure of the error signal coming from these sensors. Indeed, their ...Show More
A Study of the Allan Variance for Constant-Mean Nonstationary Processes
IEEE Signal Processing Letters
Cited by: Papers (7)
The Allan variance (AV) is a widely used quantity in areas focusing on error measurement as well as in the general analysis of variance for autocorrelated processes in domains such as engineering and, more specifically, metrology. The form of this quantity is widely used to detect noise patterns and indications of stability within signals. However, the properties of this quantity are not known for...Show More
Wavelet-Based Improvements for Inertial Sensor Error Modeling
The parametric estimation of stochastic error signals is a common task in many engineering applications, such as inertial sensor calibration. In the latter case, the error signals are often of complex nature, and very few approaches are available to estimate the parameters of these processes. A frequently used approach for this purpose is the maximum likelihood (ML), which is usually implemented t...Show More
This letter highlights some issues which were overlooked in a recently published paper called maximum likelihood identification of inertial sensor noise model parameters. The latter paper does not consider existing alternative methods, which specifically tackle this issue in a possibly more direct manner and, although remaining a generally valid proposal, does not appear to improve on the earlier ...Show More
Theoretical Limitations of Allan Variance-based Regression for Time Series Model Estimation
IEEE Signal Processing Letters
Cited by: Papers (33)
This letter formally proves the statistical inconsistency of the Allan variance-based estimation of latent (composite) model parameters. This issue has not been sufficiently investigated and highlighted since it is a technique that is still being widely used in practice, especially within the engineering domain. Indeed, among others, this method is frequently used for inertial sensor calibration, ...Show More
An inertial sensor calibration platform to estimate and select error models
Roberto Molinari;James Balamuta;Stéphane Guerrier;Jan Skaloud
2015 International Association of Institutes of Navigation World Congress (IAIN)
Year: 2015 | Conference Paper |
A new open-source software platform that, among others, allows to select models for inertial sensor stochastic calibration is presented in this paper. This platform consists in a package included in the statistical software R. The identification of stochastic models and estimation of model parameters is based on the method of Generalized Method of Wavelet Moments. This approach provides an extreme...Show More
An Approach for Observing and Modeling Errors in MEMS-Based Inertial Sensors Under Vehicle Dynamic
This paper studies the error behavior of low-cost inertial sensors in dynamic conditions. After proposing a method for error observations per sensor (i.e., gyroscope or accelerometer) and axes, their properties are estimated via the methodology of generalized method of wavelet moments. The developed model parameters are compared with those obtained under static conditions. Then, an attempt is pres...Show More
Generalized method of wavelet moments for inertial navigation filter design
Yannick Stebler;Stephane Guerrier;Jan Skaloud;Maria-Pia Victoria-Feser
The integration of observations issued from a satellite-based system (GNSS) with an inertial navigation system (INS) is usually performed through a Bayesian filter such as the extended Kalman filter (EKF). The task of designing the navigation EKF is strongly related to the inertial sensor error modeling problem. Accelerometers and gyroscopes may be corrupted by random errors of complex spectral st...Show More
Study of MEMS-based inertial sensors operating in dynamic conditions
Yannick Stebler;Stéphane Guerrier;Jan Skaloud;Roberto Molinari;Maria-Pia Victoria-Feser
2014 IEEE/ION Position, Location and Navigation Symposium - PLANS 2014
Year: 2014 | Conference Paper |
Cited by: Papers (3)
This paper aims at studying the behaviour of the errors coming from inertial sensors when measured in dynamic conditions. After proposing a method for constructing the error process, the properties of these errors are estimated via the Generalized Method of Wavelets Moments methodology. The developed model parameters are compared to those obtained under static conditions. Finally an attempted is p...Show More
A framework for inertial sensor calibration using complex stochastic error models
Yannick Stebler;Stéphane Guerrier;Jan Skaloud;Maria-Pia Victoria-Feser
Proceedings of the 2012 IEEE/ION Position, Location and Navigation Symposium
Year: 2012 | Conference Paper |
Cited by: Papers (17)
Modeling and estimation of gyroscope and accelerometer errors is generally a very challenging task, especially for low-cost inertial MEMS sensors whose systematic errors have complex spectral structures. Consequently, identifying correct error-state parameters in a INS/GNSS Kalman filter/smoother becomes difficult when several processes are superimposed. In such situations, the classical identific...Show More
Fault Detection and Isolation in Multiple MEMS-IMUs Configurations
Stephane Guerrier;Adrian Waegli;Jan Skaloud;Maria-Pia Victoria-Feser
This research presents methods for detecting and isolating faults in multiple micro-electro-mechanical system inertial measurement unit (MEMS-IMU) configurations. First, geometric configurations with n sensor triads are investigated. It is proved that the relative orientation between sensor triads is irrelevant to system optimality in the absence of failures. Then, the impact of sensor failure or ...Show More
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