Simplification of calibration of low-cost MEMS accelerometer and its temperature compensation without accurate laboratory equipment

Khankalantary, Saeed; Ranjbaran, Saeed; Ebadollahi, Saeed

doi:10.1088/1361-6501/abd0bf

Cited by 20 publications

(8 citation statements)

References 39 publications

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“…Zhang adopted the method of finite element analysis to identify the parameters of the temperature drift model to realize the compensation of the capacitive MEMS accelerometer [ 21 ]. Khankalantary studied the relationship between the error coefficients and temperature and employed cubic spline interpolation to model the relationship to remove temperature effects [ 22 ]. Wang eliminated the temperature effects of MEMS resonant accelerometers with an optimized back propagation neural network (BP NN) [ 23 ].…”

Section: Introductionmentioning

confidence: 99%

Temperature Drift Compensation of a MEMS Accelerometer Based on DLSTM and ISSA

Guo

Rui-Chu

et al. 2023

Sensors

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In order to improve the performance of a micro-electro-mechanical system (MEMS) accelerometer, three algorithms for compensating its temperature drift are proposed in this paper, including deep long short-term memory recurrent neural network (DLSTM-RNN, short DLSTM), DLSTM based on sparrow search algorithm (SSA), and DLSTM based on improved SSA (ISSA). Moreover, the piecewise linear approximation (PLA) method is employed in this paper as a comparison to evaluate the impact of the proposed algorithm. First, a temperature experiment is performed to obtain the MEMS accelerometer’s temperature drift output (TDO). Then, we propose a real-time compensation model and a linear approximation model for neural network methods compensation and PLA method compensation, respectively. The real-time compensation model is a recursive method based on the TDO at the last moment. The linear approximation model considers the MEMS accelerometer’s temperature and TDO as input and output, respectively. Next, the TDO is analyzed and optimized by the real-time compensation model and the three algorithms mentioned before. Moreover, the TDO is also compensated by the linear approximation model and PLA method as a comparison. The compensation results show that the three neural network methods and the PLA method effectively compensate for the temperature drift of the MEMS accelerometer, and the DLSTM + ISSA method achieves the best compensation effect. After compensation by DLSTM + ISSA, the three Allen variance coefficients of the MEMS accelerometer that bias instability, rate random walk, and rate ramp are improved from 5.43×10−4mg, 4.33×10−5mg/s12, 1.18×10−6mg/s to 2.77×10−5mg, 1.14×10−6mg/s12, 2.63×10−8mg/s, respectively, with an increase of 96.68% on average.

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Section: Introductionmentioning

confidence: 99%

Temperature Drift Compensation of a MEMS Accelerometer Based on DLSTM and ISSA

Guo

Rui-Chu

et al. 2023

Sensors

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“…For example, the common accelerometer calibration method described by Khan and Ranj [ 18 ] uses six specific positions where the sensor axes are precisely aligned along the axis of the calibration device. The accelerometer is calibrated by a specific position with a certain reference angle.…”

Section: Introductionmentioning

confidence: 99%

Thermal Calibration of Triaxial Accelerometer for Tilt Measurement

Yuan

Tang

Zhang

et al. 2023

Sensors

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The application of MEMS accelerometers used to measure inclination is constrained by their temperature dependence, and each accelerometer needs to be calibrated individually to increase stability and accuracy. This paper presents a calibration and thermal compensation method for triaxial accelerometers that aims to minimize cost and processing time while maintaining high accuracy. First, the number of positions to perform the calibration procedure is optimized based on the Levenberg-Marquardt algorithm, and then, based on this optimized calibration number, thermal compensation is performed based on the least squares method, which is necessary for environments with large temperature variations, since calibration parameters change at different temperatures. The calibration procedures and algorithms were experimentally validated on marketed accelerometers. Based on the optimized calibration method, the calibrated results achieved nearly 100 times improvement. Thermal drift calibration experiments on the triaxial accelerometer show that the thermal compensation scheme in this paper can effectively reduce drift in the temperature range of −40 °C to 60 °C. The temperature drifts of x- and y-axes are reduced from −13.2 and 11.8 mg to −0.9 and −1.1 mg, respectively. The z-axis temperature drift is reduced from −17.9 to 1.8 mg. We have conducted various experiments on the proposed calibration method and demonstrated its capacity to calibrate the sensor frame error model (SFEM) parameters. This research proposes a new low-cost and efficient strategy for increasing the practical applicability of triaxial accelerometers.

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“…The temperature compensation of the MEMS accelerometer is achieved by simulating the deformation of the sensor chip [19]. Khankalantary used cubic spline interpolation to model the temperature dependence of the error coefficients to minimize temperature effects [20]. Yang proposed a simple mathematical model to obtain a more accurate and robust output of low-cost quartz accelerometers at high temperatures.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Temperature Drift Compensation Method of a MEMS Accelerometer Based on Deep GRU and Optimized Monarch Butterfly Algorithm

Guo

Rui-Chu

et al. 2023

IEEE Access

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In recent years, inertial sensors based on Micro-Electro-Mechanical Systems (MEMS) have become increasingly popular. They have been widely used in various fields due to their low cost, small size, and low power consumption. It seems that MEMS inertial sensors may eventually fully occupy the middle to lower end inertial navigation application market that traditional inertial sensors previously occupied. To realize the full potential of MEMS inertial sensors, one of the critical issues is their temperature drift. This paper first proposed a recursive model for real-time compensation. Then three algorithms were proposed, including a two-layer deep gated recurrent unit recurrent neural network (GRU-RNN, short GRU), deep GRU based on monarch butterfly algorithm (MBA), and deep GRU based on optimized monarch butterfly algorithm (OMBA). Each of these three algorithms is combined with the real-time compensation model to compensate for the temperature drift of a MEMS accelerometer. The experimental results proved the correctness of these three methods, and the MEMS accelerometer's temperature drift is compensated effectively. The results indicate that the deep GRU + OMBA shows the best performance for the temperature drift compensation combined with the real-time compensation model. After deep GRU + OMBA method compensation, the angle random walk, the bias instability, the rate random walk, and rate ramp of the MEMS accelerometer were improved from 4.97e −4 mg • s 1 2 , 4.90e −4 mg, 5.57e −5 mg/s 1 2 , 1.82e −6 mg /s to 3.90e −5 mg •s 1 2 , 1.07e −5 mg, 1.12e −6 mg/s 1 2 , 3.59e −8 mg /s, respectively. Their percentage of improvement reaches 96.50% on average. INDEX TERMSDeep GRU, MEMS accelerometer, real-time compensation, OMBA, temperature drift.

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Simplification of calibration of low-cost MEMS accelerometer and its temperature compensation without accurate laboratory equipment

Cited by 20 publications

References 39 publications

Temperature Drift Compensation of a MEMS Accelerometer Based on DLSTM and ISSA

Temperature Drift Compensation of a MEMS Accelerometer Based on DLSTM and ISSA

Thermal Calibration of Triaxial Accelerometer for Tilt Measurement

Real-Time Temperature Drift Compensation Method of a MEMS Accelerometer Based on Deep GRU and Optimized Monarch Butterfly Algorithm

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