MEMS technology evaluation for submerged vehicle navigation

• Autorzy: Demkowicz J.
• Języki publikacji: EN

Abstrakty

EN

The article undertakes analysis of some vital aspects of inertial navigation using MEMS. Although MEMS inertial sensors offer affordable, scaled units, and though their inherent measurement noise can be relatively easily mitigated, there are still parameters due to which they are not currently capable of meeting all requirements for accurate inertial navigation. The article presents a few aspects of MEMS gyro errors, and their estimation process in the context of INS processing flow. These errors have a serious impact on overall inertial system performance. The results of undertaken researches in that area, and pointing out the main difficulties behind the INS when using a few top MEMS technologies, were presented as well. The paper clearly states, that current MEMS technologies, including sophisticated software, does not fulfil submerged inertial navigation whilst operating in dynamic conditions, due to linear acceleration, affecting gyro performance.

Słowa kluczowe

EN

MEMS; IMU; INS

• Wydawca: Polskie Towarzystwo Akustyczne. Oddział Gdański
• Czasopismo: Hydroacoustics
• Rocznik: 2017
• Tom: Vol. 20
• Strony: 35--40
• Opis fizyczny: Bibliogr. 17 poz., rys., tab.

Twórcy

autor

Demkowicz J.

• Gdansk University of Technology Gdansk, Narutowicza str. 11/12, Poland

Bibliografia

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• [3] J.W. Valvano, Introduction to Embedded Systems, ISBN: 978-1477508992.
• [4] IEEE Std 952-1997, "Guide and Test Procedure for Single Axis Interferometric Fiber Optic Gyros," IEEE.
• [5] S. Y. Chen, “Kalman filter for robot vision: a survey,” IEEE Transactions on Industrial Electronics, vol. 59, no. 11, pp. 4409– 4420, 2012.
• [6] A. H. Mohamed and K. P. Schwarz, “Adaptive Kalman filtering for INS/GPS,” Journal of Geodesy, vol. 73, no. 4, pp. 193–203, 1999.
• [7] L. B. Chang, B. Q. Hu, A. Li, and F. J. Qin, “Strapdown inertial navigation system alignment based on marginalized unscented Kalman filter,” IET Science, Measurement and Technology, vol. 7, no. 2, pp. 128–138, 2013.
• [8] H. Qian, Q. Xia, B. Jiang, and C. Wang, “On modeling of random drift of MEMS gyroscope and design of Kalman filter,” in Proceedings of the IEEE International Conference on Mechatronics and Automation (ICMA ’09), pp. 4355–4360, August 2009.
• [9] Analog Devices Technical Articles, www.analog.com
• [10] David W. Allan, John H. Shoaf and Donald Halford: Statistics of Time and Frequency Data Analysis, NBS Monograph 140, pages 151–204, 1974, http://tf.boulder.nist.gov/general/pdf/59.pdf
• [11] http://www.nxp.com/ application notes
• [12] Analog Devices Technical Zone, https://ez.analog.com
• [13] IEEE-STD-952-1997, Appendix B, provides a lot of detailed information on In-run Bias
• [14] https://ez.analog.com/docs/DOC-2162, In-run stability
• [15] J. Demkowicz, Particle Filter Modification using Kalman Optimal Filtering Method as Applied to Road Detection from Satellite Images, MIKON 2014, 20th International Conference on Microwaves, Radar and Wireless Communications.
• [16] J. Demkowicz, K. Bikonis, MEMS Technology Quality Requirements as Applied to a Multibeam Echosounder, 2016 HYDROACOUSTICS ANNUAL JOURNAL, pp. 75- 82, vol. 19, 2016.
• [17] http://www.oceaneering.com/rovs/rov-systems/omnimaxx/

Uwagi

Opracowanie w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).