Real-Time Autocalibration of MEMS Accelerometers

Glueck, Manuel; Oshinubi, Dayo; Schopp, Patrick; Manoli, Yiannos

doi:10.1109/tim.2013.2275240

Cited by 37 publications

(23 citation statements)

References 19 publications

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“…n . Then, based on (25) and (26), Eq. (24) can be rearranged as: (27) where v n is the hidden variable for penalized EM algorithm.…”

Section: Online Calibration Methods For Linearized 9-parameter Modelmentioning

confidence: 99%

“…In real life situation, to minimize the effect of environmental temperature, daily drift and so on, some online calibration methods are proposed. Several optimization methods [23], [24], [26] are selected to solve this problem based on unscented Kalman filter (UKF) [23], [26] or extended Kalman filter [24]. However, most of these methods are not based on auto-calibration; precise orientation information is still required.…”

Section: Linearization Of Ta 9-parameter Model 55mentioning

confidence: 99%

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Online auto-calibration of triaxial accelerometer with time-variant model structures

Argha

Celler

et al. 2017

Sensors and Actuators A: Physical

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In this paper, an online auto-calibration method for MicroElectroMechanical Systems (MEMS) triaxial accelerometer (TA) is proposed, which can simultaneously identify the time-dependent model structure and its parameters during the changes of the operating environment. Firstly, the model as well as its associated cost function is linearized by a new proposed linearization approach. Then, exploiting an online sparse recursive least square (SPARLS) estimation, the unknown parameters are identified. In particular, the online sparse recursive method is based on an L 1-norm penalized expectation-maximum (EM) algorithm, which can amend the model automatically by penalizing the insignificant parameters to zero. Furthermore, this method can reduce computational complexity and be implemented in a low-cost Micro-Controller-Unit (MCU). Based on the numerical analysis, it can be concluded that the proposed recursive algorithm can calculate the unknown parameters reliably and accurately for most MEMS triaxial accelerometers available in the market. Additionally, this method is experimentally validated by comparing the output estimations before and after calibration under various scenarios, which further confirms its feasibility and effectiveness for online TA calibration.

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“…n . Then, based on (25) and (26), Eq. (24) can be rearranged as: (27) where v n is the hidden variable for penalized EM algorithm.…”

Section: Online Calibration Methods For Linearized 9-parameter Modelmentioning

confidence: 99%

Section: Linearization Of Ta 9-parameter Model 55mentioning

confidence: 99%

Online auto-calibration of triaxial accelerometer with time-variant model structures

Argha

Celler

et al. 2017

Sensors and Actuators A: Physical

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“…For multiple-input multiple-output systems, the Kalman filter has proven to be an appropriate choice as state observer for a large variety of applications, such as localization [52], orientation estimation [53], or online calibration [54]. Its main advantage is that the state estimation process incorporates the probabilistic nature of the system and the sensor measurements.…”

Section: State Estimationmentioning

confidence: 99%

Observing Relative Motion With Three Accelerometer Triads

Schopp

Graf

Maurer

et al. 2014

IEEE Trans. Instrum. Meas.

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A gyroscope-free inertial measurement unit (GF-IMU) detects the relative motion of a body based on acceleration measurements only. It consists of multiple transducers attached at distinct locations within the body that together form an accelerometer array. In this paper, we employ only three accelerometer triads in order to completely capture the transversal and angular acceleration as well as the angular velocity. By modeling the GF-IMU as a nonlinear control system, we are able to conduct an observability analysis, which shows that this approach is capable of capturing an arbitrary spatial motion. We also show that additional triads only provide redundant information. Based on the control system formulation, we derive the models required to employ a nonlinear Kalman filter as a state observer. As the system description is of a general form they are suitable for any accelerometer array regardless of the number and placement of the transducers. Hence, the presented Kalman filter approach is applicable to all observable GF-IMU configurations. The measurements taken with a prototype on a 3-D rotation table confirm the observability analysis. The evaluations also show that the approach using three accelerometer triads achieves an estimation accuracy comparable with that of arrays employing a higher number of triads.

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“…The adjustment is done in a frame specified by the physical alignment of the sensor triad. The proposed method exploits the magnetic field of the Earth as reference signal enabling the adjustment to a [4] acc constant position and zero manual motion F no equipment needed gyro velocity of sensors [5] acc gravity mis positions F no equipment needed gyro calibrated acc measurements mis arbitrary motion [6] acc gravity, mis positions L sensors not adjusted gyro angular velocity mis rate table to the same frame [7] acc gravity, centripetal mis positions L gyro acceleration, angular velocity mis, g-dep rate table [8] acc gravity mis positions F (acc) sensors not adjusted gyro angular velocity mis rate table L (gyro) to the same frame [9] acc gravity positions, F gyro known rotation angles manual rotations [10] acc gravity mis positions F [11], [12] acc gravity arbitrary movements F [13] acc magnitude of gravity unknown positions F [14] gyro calibrated acc and/or mag mis rotations F measurements [15] gyro pseudo observations and acc arbitrary movements F no equipment needed and mag measurements [16] acc known attitude or calibrated mis arbitrary rotation F mag sensor measurements mis, hi, si [17], [18] mag magnetic field of the Earth mis positions, L reference magnetometer [19] mag magnetic field of the Earth mis, hi, si rotations F [20] mag magnetic field of the Earth mis, hi, si arbitrary motion F [21] acc gravity mis positions F additional measurements gyro calibrated acc or mag meas. mis arbitrary motion needed to adjust the mag magnetic field of the Earth mis, hi, si positions sensors to the same frame [22] acc gravity mis positions L gyro known positions mis mag magnetic field of the Earth mis, hi, si [23] acc magnitude of gravity, mis unknown positions F all the sensors gyro orientation differences, mis and rotations calibrated mag magnitude of magnetic mis, hi, si to the same frame field of the Earth Proposed acc gravity mis positions F all the sensors method gyro known net rotations mis and rotations calibrated at once mag magnetic field of the Earth mis, hi, si to the same frame Note: Calibration parameters in addition to scale-factors and biases.…”

Section: Introductionmentioning

confidence: 99%

A Multi-Position Calibration Method for Consumer-Grade Accelerometers, Gyroscopes, and Magnetometers to Field Conditions

et al. 2017

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This paper presents a calibration method for consumer-grade accelerometers, gyroscopes, and magnetometers. Considering the calibration of consumer-grade sensors, it is essential that no specialized equipment is required to create reference signals. In addition, the less is required from the reference signals, the more suitable the method is on the field. In the proposed method, the novelty in the calibration of the gyroscopes lies in the exploitation of only the known net rotations between the positions in a multi-position calibration. For accelerometers and magnetometers, the innovation is that the direction of reference signals, the gravity and the magnetic field of the Earth, are estimated with calibration parameters. As a consequence, no precise absolute alignment of the sensors is needed in the calibration. The rotations need not be done about a constant axis. In the proposed method, the biases, scale factors, misalignments, and cross-coupling errors for all the sensors as well as hard iron and soft iron effect for magnetometers were modelled. In addition, the drift of the sensors during the calibration was estimated. As a result, all the sensors were calibrated at once to the same frame, exploiting only a cube and a jig and thus, the method is eligible in the field. To estimate the quality of the calibration results, 95 % confidence intervals were calculated for the calibration parameters. Simulations were done to indicate that the calibration method is unbiased.Index Terms-Multi-position calibration, inertial measurement unit (IMU), accelerometer, gyroscope, magnetometer, confidence interval. This is the author's version of an article that has been published in this journal. Changes were made to this version by the publisher prior to publication.The final version of record is available at http://dx.

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Real-Time Autocalibration of MEMS Accelerometers

Cited by 37 publications

References 19 publications

Online auto-calibration of triaxial accelerometer with time-variant model structures

Online auto-calibration of triaxial accelerometer with time-variant model structures

Observing Relative Motion With Three Accelerometer Triads

A Multi-Position Calibration Method for Consumer-Grade Accelerometers, Gyroscopes, and Magnetometers to Field Conditions

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