Error Modeling and Analysis of Inertial Measurement Unit Using Stochastic and Deterministic Techniques

Abstract:

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The measurements provided by inertial measurement unit (IMU) are erroneous due to certain noise parameters which are needed to be taken into account because the corrupted data is of little practical value in inertial navigation systems (INS). By integrating the IMU data in navigation algorithm, these errors are accumulated, leading to significant drift in the attitude, position and velocity outputs. Several techniques have been devised for the error modeling of this error by way of Neural Networks (NNs), PSD, ARMA, etc. In this paper, the deterministic and stochastic approach is followed to model the noise parameters of a low cost IMU. The error parameters thus determined by using the both techniques help in the development of an effective navigation algorithm. Deterministic errors are calculated by the help of Up-Down Test and the Rate Table test. While the stochastic errors, which are more random in nature, are recognized using Power Spectral Density (PSD) Analysis and Allan Variance techniques.

Info:

Periodical:

Advanced Materials Research (Volumes 403-408)

Edited by:

Li Yuan

Pages:

4447-4455

DOI:

10.4028/www.scientific.net/AMR.403-408.4447

Citation:

Z. Ashraf and W. Nafees, "Error Modeling and Analysis of Inertial Measurement Unit Using Stochastic and Deterministic Techniques", Advanced Materials Research, Vols. 403-408, pp. 4447-4455, 2012

Online since:

November 2011

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Price:

$35.00

[1] Vikas Kumar N, Integration of inertial navigation system and global positioning system using Kalman filtering, M. Tech Dissertation, Indian Institute of Technology, Bombay. July (2004).

[2] Haiying Hou, Modeling inertial sensor errors using Allan variance, " UCEGE reports number 20201. Master, s Thesis, University of Calgary, September (2004).

[3] Niklas Hjortsmarker, Experimental system for validating GPS/INS integration algorithms, " Master, s Thesis, Lulea University of Technology, (2005).

[4] Minha Park and Yang Gao, Error analysis and stochastic modeling of low-cost MEMS accelerometer, J. Intell Robot Syst , vol. 46, no. 1, pp- 27-41.

DOI: 10.1007/s10846-006-9037-5

[5] Senter Technology, MEMS IMU Calibration Example.

[6] Naser El-Sheimy, Haiying Hou and Xiaoji Niu, Analysis and Modeling of Inertial Sensors using Allan Variance, IEEE Transactions on Instrumentation and Measurement, Vol. 57, no. 1, January (2008).

DOI: 10.1109/tim.2007.908635

[7] Wang Hao and Weifeng Tian, Modeling the random drift of micro-machined gyroscope with neural network, Neural Processing Letters, 2005, Vol. 22, no. 3, pp-235-247.

DOI: 10.1007/s11063-005-6800-8

[8] Guoqiang Xu and Xiuyun Meng, The MEMS IMU error modeling analysis using support vector machines, Second International Symposium on Knowledge Acquisition and Modeling, 2009, pp-335-337.

DOI: 10.1109/kam.2009.287

[9] Chen Xiyuan, Modeling random gyro drift by time series neural retwork and by traditional method, Int. Conference on Neural Networks and Signal Processing, vol. 1, pp-810-813, (2003).

DOI: 10.1109/icnnsp.2003.1279399

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