Allan variance the stability analysis algorithm for MEMS based inertial sensors stochastic error

Bhardwaj, Renu; Kumar, V. K.; Kumar, Neelesh

doi:10.1109/iemcon.2015.7344524

Cited by 5 publications

(2 citation statements)

References 16 publications

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“…The stochastic coefficients are extracted from the AV plot, as reported in Table 7. Obtained stochastic coefficient of the inertial sensor’s error model are within the 1σ confidence of interval, as per the datasheet specifications with minor variation (Bhardwaj et al, 2015). The estimated error of gyroscope is symmetrical around the mean value, but accelerometer is non-symmetrical, it is deeper in negative side.…”

Section: Resultsmentioning

confidence: 83%

Errors in micro-electro-mechanical systems inertial measurement and a review on present practices of error modelling

Bhardwaj

Kumar

2017

Transactions of the Institute of Measurement and Control

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Micro-electro-mechanical systems (MEMS) technology-based accelerometers and gyroscopes are small size, mass produced, low cost inertial sensors, which are now being used in aerospace, underwater vehicles, automotive, robotics, mobiles, gaming consoles, prosthetic devices and many other applications. MEMS inertial sensors are available in many grades in market and selecting the appropriate grade sensor is very important. Owing to interaction of different types of energies, different noises are generated in MEMS devices; these noises cause significant change in output and the first section of this paper illustrates that. In application, where MEMS inertial sensors are used, the accuracy, repeatability and reproducibility of inertia measurement is probed primarily by complex testing, using extensive range of physical stimuli. Noises in inertial measurement are generally dealt by designing a unit measurement model. Noises are treated as additive error in linear unit model and are modelled using various techniques so that errors can be compensated to improve the accuracy. This paper reviews the theory, framework and methodology used in the error model of a MEMS inertial sensor and stochastic modelling of measurement. Experimental results from the most commonly used Allan variance techniques are discussed. Error modelling methodology, consisting of testing and calibration methods, designing thermal model, stochastic modelling and parameter estimation techniques, is illustrated. Figures and tables under each section summarize features, merits, limitation and future research scope. This paper should serve as a single reference for researchers and engineers working on application specific system design and instrumentation using MEMS inertial sensors. Conclusion from the study should help in selecting the appropriate grade of sensor as well as the best error modelling as per the trade-off existing between accuracy and development cost of error modelling.

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Section: Resultsmentioning

confidence: 83%

Errors in micro-electro-mechanical systems inertial measurement and a review on present practices of error modelling

Bhardwaj

Kumar

2017

Transactions of the Institute of Measurement and Control

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“…Analyzing random errors in inertial sensors is feasible for improving the accuracy of inertial navigation systems. The traditional methods of analyzing random errors include the power spectral density [ 36 ], autocorrelation analysis [ 37 ], and the Allan variance [ 38 ]. The Allan variance is widely used because it is able to distinguish different error sources and can be calculated and separated easily.…”

Section: Methodsmentioning

confidence: 99%

An Optimal Enhanced Kalman Filter for a ZUPT-Aided Pedestrian Positioning Coupling Model

Fan

Zhang

Sun

et al. 2018

Sensors

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Aimed at overcoming the problems of cumulative errors and low positioning accuracy in single Inertial Navigation Systems (INS), an Optimal Enhanced Kalman Filter (OEKF) is proposed in this paper to achieve accurate positioning of pedestrians within an enclosed environment. Firstly, the errors of the inertial sensors are analyzed, modeled, and reconstructed. Secondly, the cumulative errors in attitude and velocity are corrected using the attitude fusion filtering algorithm and Zero Velocity Update algorithm (ZUPT), respectively. Then, the OEKF algorithm is described in detail. Finally, a pedestrian indoor positioning experimental platform is established to verify the performance of the proposed positioning system. Experimental results show that the accuracy of the pedestrian indoor positioning system can reach 0.243 m, giving it a high practical value.

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Modeling of MEMS gyro drift based on wavelet threshold denoising and improved Elman neural network

Zhang

Gao

Cai

2019

2019 14th IEEE International Conference on Electronic Measurement &Amp; Instruments (ICEMI)

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Allan variance the stability analysis algorithm for MEMS based inertial sensors stochastic error

Cited by 5 publications

References 16 publications

Errors in micro-electro-mechanical systems inertial measurement and a review on present practices of error modelling

Errors in micro-electro-mechanical systems inertial measurement and a review on present practices of error modelling

An Optimal Enhanced Kalman Filter for a ZUPT-Aided Pedestrian Positioning Coupling Model

Modeling of MEMS gyro drift based on wavelet threshold denoising and improved Elman neural network

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