Temperature-Dependent Calibration of Triaxial Sensors: Algorithm, Prototype, and Some Results

Bernal-Polo, Pablo; Barberá, Humberto Martínez

doi:10.1109/jsen.2019.2946016

Cited by 10 publications

(7 citation statements)

References 12 publications

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“…Table 2 lists the gyroscope characteristics of consumer-level (low level) IMU, MPU-9255/9150 [ 14 , 15 ]. Table 3 and Table 4 provide the gyroscope characteristics of high-level inertial measurement units ADI16367 [ 16 ] and IMU-440 [ 17 ].…”

Section: Main Error Sources Of Mems Gyroscopementioning

confidence: 99%

“…Additionally, the introduced gyroscope in-self calibration studies lack research work in analyzing the main error sources of MEMS sensor and providing theoretical calibration experiment design principles. Pablo [ 14 ] introduces a temperature-dependent calibration algorithm to calibrate triaxial sensors and develops an electromechanical prototype to perform the approach. Compared with this algorithm, our method does not need to consider the temperature element as the whole calibration procedure can be accomplished in short time without temperature variation and as such gyroscope bias error does not vary.…”

Section: Introductionmentioning

confidence: 99%

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A Novel MEMS Gyroscope In-Self Calibration Approach

Zhou

et al. 2020

Sensors

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This paper presents a novel approach for hand-held low-cost MEMS (micro-electro-mechanical system) gyroscope in-self calibration. This method does not need the support of external high-precision equipment compared with traditional calibration scheme and can be accomplished by user hand rotation. In this approach, Kalman filter is designed to perform the calibration procedure and estimate gyroscope bias error, scale factor error and non-orthogonal error. The system observability is analyzed and the dynamic rotating conditions under which the sensor errors become observable are derived. The design principles of optimal calibration procedure are provided as well. Both simulated and practical experiments are carried out to test the validation of the proposed calibration algorithm. The achieved results demonstrate that the introduced approach can provide promising calibration scheme for the low-cost MEMS gyroscope.

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Section: Main Error Sources Of Mems Gyroscopementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Novel MEMS Gyroscope In-Self Calibration Approach

Zhou

et al. 2020

Sensors

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“…Establishing a suitable IMU calibration model is a key technology for error compensation [ 3 , 4 ]. Therefore, it is necessary to establish an IMU error compensation model which meets the accuracy requirements and calibrate it accurately to improve the SINS accuracy [ 5 , 6 , 7 ]. The IMU mathematical model is divided into the static mathematical model, dynamic mathematical model, and random mathematical model.…”

Section: Introductionmentioning

confidence: 99%

Research on IMU Calibration Model Based on Polar Decomposition

et al. 2023

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As an important deterministic error of the inertial measurement unit (IMU), the installation error has a serious impact on the navigation accuracy of the strapdown inertial navigation system (SINS). The impact becomes more severe in a highly dynamic application environment. This paper proposes a new IMU calibration model based on polar decomposition. Using the new model, the installation error is decomposed into a nonorthogonal error and a misalignment error. The compensation of the IMU calibration model is decomposed into two steps. First, the nonorthogonal error is compensated, and then the misalignment error is compensated. Based on the proposed IMU calibration model, we used a three-axis turntable to calibrate three sets of strapdown inertial navigation systems (SINS). The experimental results show that the misalignment errors are larger than the nonorthogonal errors. Based on the experimental results, this paper proposes a new method to simplify the installation error. This simplified method defines the installation error matrix as an antisymmetric matrix composed of three misalignment errors. The navigation errors caused by the proposed simplified calibration model are compared with the navigation errors caused by the traditional simplified calibration model. The 48-h navigation experiment results show that the proposed simplified calibration model is superior to the traditional simplified calibration model in attitude accuracy, velocity accuracy, and position accuracy.

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“…Three-axis accelerometers have been widely used to monitor aircraft machine vibrations because of their small size, light weight, large dynamic measurement range and good adaptability to hostile environments (Xu et al, 2020;Bernal-Polo and Martinez-Barbera, 2019;Liu et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Sensitivity calibration of a three-axis accelerometer under different temperature conditions using the hybrid GA–PSO–BPNN algorithm

Kong

2021

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Purpose Three-axis accelerometers play a vital role in monitoring the vibrations in aircraft machinery, especially in variable flight temperature environments. The sensitivity of a three-axis accelerometer under different temperature conditions needs to be calibrated before the flight test. Hence, the authors investigated the efficiency and sensitivity calibration of three-axis accelerometers under different conditions. This paper aims to propose the novel calibration algorithm for the three-axis accelerometers or the similar accelerometers. Design/methodology/approach The authors propose a hybrid genetic algorithm–particle swarm optimisation–back-propagation neural network (GA–PSO–BPNN) algorithm. This method has high global search ability, fast convergence speed and strong non-linear fitting capability; it follows the rules of natural selection and survival of the fittest. The authors describe the experimental setup for the calibration of the three-axis accelerometer using a three-comprehensive electrodynamic vibration test box, which provides different temperatures. Furthermore, to evaluate the performance of the hybrid GA–PSO–BPNN algorithm for sensitivity calibration, the authors performed a detailed comparative experimental analysis of the BPNN, GA–BPNN, PSO–BPNN and GA–PSO–BPNN algorithms under different temperatures (−55, 0 , 25 and 70 °C). Findings It has been showed that the prediction error of three-axis accelerometer under the hybrid GA–PSO–BPNN algorithm is the least (approximately ±0.1), which proved that the proposed GA–PSO–BPNN algorithm performed well on the sensitivity calibration of the three-axis accelerometer under different temperatures conditions. Originality/value The designed GA–PSO–BPNN algorithm with high global search ability, fast convergence speed and strong non-linear fitting capability has been proposed to decrease the sensitivity calibration error of three-axis accelerometer, and the hybrid algorithm could reach the global optimal solution rapidly and accurately.

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Temperature-Dependent Calibration of Triaxial Sensors: Algorithm, Prototype, and Some Results

Cited by 10 publications

References 12 publications

A Novel MEMS Gyroscope In-Self Calibration Approach

A Novel MEMS Gyroscope In-Self Calibration Approach

Research on IMU Calibration Model Based on Polar Decomposition

Sensitivity calibration of a three-axis accelerometer under different temperature conditions using the hybrid GA–PSO–BPNN algorithm

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