High-precision algorithmic compensation of temperature instability of accelerometer’s scaling factor

Alaluev, R. V.; Ivanov, Yu. V.; Malyutin, D. M.; Raspopov, V. Ya.; Dmitriev, V.; Ermilov, S. P.; Ermilova, G. A.

doi:10.1134/s0005117911040175

Cited by 10 publications

(5 citation statements)

References 1 publication

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“…In addition to the above methods, some researchers also improve the output accuracy by improving the physical structure and materials of sensors or designing compensation circuits. This method is highly specialized, difficult, and usually requires huge research funding [ 32 , 33 , 34 , 35 ]. In fact, the source of the output error of the sensor is multi-faceted and uncontrollable, so it is difficult to completely eliminate the output error by improving the hardware.…”

Section: Introductionmentioning

confidence: 99%

Research on Random Drift Model Identification and Error Compensation Method of MEMS Sensor Based on EEMD-GRNN

Shi

Fang

Xue

et al. 2022

Sensors

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Random drift error is one of the important factors of MEMS (micro-electro-mechanical-system) sensor output error. Identifying and compensating sensor output error is an important means to improve sensor accuracy. In order to reduce the impact of white noise on neural network modeling, the ensemble empirical mode decomposition (EEMD) method was used to separate white noise from the original signal. The drift signal after noise removal is modeled by GRNN (general regression neural network). In order to achieve a better modeling effect, cross-validation and parameter optimization algorithms were designed to obtain the optimal GRNN model. The algorithm is used to model and compensate errors for the generated random drift signal. The results show that the mean value of original signal decreases from 0.1130 m/s2 to −1.2646 × 10−7 m/s2, while the variance decreases from 0.0133 m/s2 to 1.0975 × 10−5 m/s2. In addition, the displacement test was carried out by MEMS acceleration sensor. Experimental results show that the displacement measurement accuracy is improved from 95.64% to 98.00% by compensating the output error of MEMS sensor. By comparing the GA-BP (genetic algorithm-back propagation) neural network and the polynomial fitting method, the EEMD-GRNN method proposed in this paper can effectively identify and compensate for complex nonlinear drift signals.

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Section: Introductionmentioning

confidence: 99%

Research on Random Drift Model Identification and Error Compensation Method of MEMS Sensor Based on EEMD-GRNN

Shi

Fang

Xue

et al. 2022

Sensors

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“…Performances of the RLG and the accelerometer can be improved through advances in manufacturing techniques, inner structural optimization and the usage of new materials. In addition, it is also effective to improve the accuracy of inertial sensors by reducing the impacts of temperature fluctuation [3]. Since the RLG is an optical sensor which is mainly made up of thermal-insensitive materials, the mechanical accelerometer is more easily influenced by temperature variation [4].…”

Section: Introductionmentioning

confidence: 99%

Precision Temperature Control for the Laser Gyro Inertial Navigation System in Long-Endurance Marine Navigation

Xiong

Wei

Gao

et al. 2021

Sensors

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In the Ring Laser Gyro Inertial Navigation System (RLG INS), the temperature characteristics of the accelerometer can directly influence the measurement results. In order to improve navigation accuracy in long-endurance marine navigation, the operating temperature of the accelerometer should be precisely controlled. Based on thermal studies on the accelerometer, temperature control precision should be better than 0.01 °C to achieve 1 × 10−5 m/s2 output accuracy of the accelerometer. However, this conclusion is obtained by approximate calculations and cannot be directly applied to different inertial navigation systems. In order to verify this thermal conclusion and broaden its application, the Back Propagation Neural Network (BP-NN) algorithm is adopted to validate the feasibility of temperature control in this paper. In addition, a multi-level temperature control system is also set up and carefully designed to support the validation and experiments under different conditions. Test results of the temperature control system prove that operating temperature variation can be reduced to 0.01 °C. Meanwhile, the standard deviation per hundred seconds of the accelerometer outputs, after temperature control, reaches 1 × 10−5 m/s2. Static altitude and navigation results were improved by 41.97% and 62.91%, respectively, with the precision temperature control system, which meets the long-endurance marine navigation requirements.

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“…Quartz accelerometers are typical high-precision mechanical and electrical products that are widely used in aerospace, transportation, and other fields in China. The quartz accelerometer consists of a servo-integrated hybrid circuit part, a process structure part (quartz pendulum), and a basic material part (viscose, magnetic material) [1] [2]. The composition material and the process are complex, and the accuracy is required to be high.…”

Section: Introductionmentioning

confidence: 99%

Storage Life Evaluation of Quartz Accelerometer Servo Circuit Based on Drift Brownian Movement

Pan¹,

Luo²,

Li³

et al. 2018

dtetr

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The research object of this paper was the servo circuit of quartz accelerometer. The evaluation method based on drift Brownian movement was proposed to evaluate the storage reliability and storage life of servo circuit. Based on the characteristic of the servo circuit of quartz accelerometer, an accelerated model was established. Then, the reliability evaluation model was generated combined with linear drift Brownian movement. Using the degradation data of servo circuit acceleration storage, the storage life of servo circuit was evaluated. The evaluated results were reasonable, and the feasibility of the method was validated.

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High-precision algorithmic compensation of temperature instability of accelerometer’s scaling factor

Cited by 10 publications

References 1 publication

Research on Random Drift Model Identification and Error Compensation Method of MEMS Sensor Based on EEMD-GRNN

Research on Random Drift Model Identification and Error Compensation Method of MEMS Sensor Based on EEMD-GRNN

Precision Temperature Control for the Laser Gyro Inertial Navigation System in Long-Endurance Marine Navigation

Storage Life Evaluation of Quartz Accelerometer Servo Circuit Based on Drift Brownian Movement

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