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A feedback weighted fusion algorithm with dynamic sensor bias correction for gyroscope array

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Low-cost Micro-Electromechanical System (MEMS) gyroscopes are known to have a smaller size, lower weight, and less power consumption than their more technologically advanced counterparts. However, current low-grade MEMS gyroscopes have poor performance and cannot compete with quality sensors in high accuracy navigational and guidance applications. The main focus of this paper is to investigate performance improvements by fusing multiple homogeneous MEMS gyroscopes. These gyros are transformed into a virtual gyro using a feedback weighted fusion algorithm with dynamic sensor bias correction. The gyroscope array combines eight homogeneous gyroscope units on each axis and divides them into two layers of differential configuration. The algorithm uses the gyroscope array estimation value to remove the gyroscope bias and then correct the gyroscope array measurement value. Then the gyroscope variance is recalculated in real time according to the revised measurement value and the weighted coefficients and state estimation of each gyroscope are deduced according to the least square principle. The simulations and experiments showed that the proposed algorithm could further reduce the drift and improve the overall accuracy beyond the performance limitations of individual gyroscopes. The maximum cumulative angle error was -2.09 degrees after 2000 seconds in the static test, and the standard deviation (STD) of the output fusion value of the proposed algorithm was 0.006 degrees/s in the dynamic test, which was only 1.7% of the STD value of an individual gyroscope.
Rocznik
Strony
161--179
Opis fizyczny
Bibliogr. 32 poz., rys., tab., wykr., wzory
Twórcy
autor
  • School of Automation, Northwestern Polytechnical University, Xi’an 710129, China
autor
  • School of Automation, Northwestern Polytechnical University, Xi’an 710129, China
autor
  • Xi’an Research Institute of High Technology, Hongqing Town, Xi’an 710025, China
autor
  • Xi’an Research Institute of High Technology, Hongqing Town, Xi’an 710025, China
Bibliografia
  • [1] Wang, Y. L., Soltani, M., & Hussain, D. M. A. (2017). An attitude heading and reference system for marine satellite tracking antenna. IEEE Transactions on Industrial Electronics, 64(4), 3095-3104. https://doi.org/10.1109/TIE.2016.2633529
  • [2] Hua, M. D., Ducard, G., Hamel, T., Mahony, R, & Rudin, K. (2014). Implementation of a nonlinear attitude estimator for aerial robotic vehicles. IEEE Transactions on Control Systems Technology, 22(1), 201-213. https://doi.org/10.1109/TCST.2013.2251635
  • [3] Vittorio, M. N. P., Antonello, C., Lorenzo, V., Martino, D. C., & Carlo, E. C. (2017). Gyroscope technology and applications: a review in the industrial perspective. Sensors, 17(10), 2284-2305. https://doi.org/10.3390/s17102284
  • [4] Shen, X. W., Yao, M. L., Jia, W. M., & Yuan, D. (2012). Adaptive complementary filter using fuzzy logic and simultaneous perturbation stochastic approximation algorithm. Measurement, 45(5), 1257-1265. https://doi.org/10.1016/j.measurement.2012.01.011
  • [5] Ghasemi-Moghadam, S., & Homaeinezhad, M. R. (2018). Attitude determination by combining arrays of MEMS accelerometers, gyros, and magnetometers via quaternion-based complementary filter. International Journal of Numerical Modelling: Electronic Networks, Devices and Fields, 31(3), 1-24. https://doi.org/10.1002/jnm.2282
  • [6] Shashi, P., Vipan, K., & Amod, K. (2017). A comprehensive overview of inertial sensor calibration techniques. Journal of Dynamic Systems, Measurement, and Control, 139(1), 1-11. https://doi.org/10.1115/1.4034419
  • [7] El-Sheimy, N., Hou, H., & Niu, X. (2008). Analysis and modeling of inertial sensors using Allan variance. IEEE Transactions on Instrumentation and Measurement, 57(1), 140-149. https://doi.org/10.1109/TIM.2007.908635
  • [8] Niu, X., Li, Y., Zhang, H., Wang, Q., & Ban, Y. (2013). Fast thermal calibration of low-grade inertial sensors and inertial measurement units. Sensors, 13(9), 12192-12217. https://doi.org/10.3390/s130912192
  • [9] El-Diasty, M., & Pagiatakis, S. (2008). Calibration and stochastic modeling of inertial navigation sensor errors. Journal of Global Positioning Systems, 7(2), 170-182. https://doi.org/10.5081/jgps.7.2.170
  • [10] Jixin, L., Ankit, A. R., Yukinori K., & Takanori E. (2016). A method of low-cost IMU calibration and alignment. Proceedings of the 2016 IEEE/SICE International Symposium on System Integration, Japan. https://doi.org/10.1109/SII.2016.7844027
  • [11] Fang, B., Chou, W. S., & Ding, L. (2014). An optimal calibration method for a MEMS inertial measurement unit. International Journal of Advanced Robotic Systems, 11, 1-14. https://doi.org/10.5772/57516
  • [12] Jean-Philippe, D., Henrik, M., & Dan R. (2006). Bias aware Kalman filters: comparison and improvements. Advances in Water Resources. 29(5), 707-718. https://doi.org/10.1016/j.advwatres.2005.07.006
  • [13] Gebre-Egziabher, D., Hayward, R. C., & Powell, J. D. (2004). Design of multi-sensor attitude determination systems. IEEE Transactions on Aerospace and Electronic Systems, 40(2), 627-649. https://doi.org/10.1109/TAES.2004.1310010
  • [14] Shen, X., Yuan, D., Chang, R., & Jin, W. (2019). A nonlinear observer for attitude estimation of vehicle-mounted satcom-on-the-move. https://doi.org/10.1109/JSEN.2019.2918381IEEE Sensors Journal, 19(18), 8057-8066.
  • [15] Wu, Z. M., Sun, Z. G., Zhang, W. Z., & Chen, Q. (2016). A novel approach for attitude estimation based on MEMS inertial sensors using nonlinear complementary filters. IEEE Sensors Journal, 16(10), 3856-3864. https://doi.org/10.1109/JSEN.2016.2532909
  • [16] Thisura, H., & Munasinghe, S. R. (2017). Accurate attitude estimation under high accelerations and magnetic disturbances. Proceedings of MERCon, Sri Lanka. https://doi.org/10.1109/MERCon.2017.7980452
  • [17] Maliňák, P., Soták, M., Kaňa, Z., Baránek, R., & Duník, J. (2018). Pure-inertial AHRS with adaptive elimination of non-gravitational vehicle acceleration. Proceedings of IEEE/ION Position, Location and Navigation Symposium (PLANS), USA. https://doi.org/10.1109/PLANS.2018.8373445
  • [18] Xing, L, Hang, Y. J., Xiong, Z., Liu, J. Y., & Wan, Z. (2016). Accurate attitude estimation using ARS under conditions of vehicle movement based on disturbance acceleration adaptive estimation and correction. Sensors, 16(10), 1716-1731. https://doi.org/10.3390/s16101716
  • [19] Simon, K. K., & Luminita-Cristiana T. (2011). Improving MEMS gyroscope performance using homogeneous sensor fusion [Master’s Thesis, Aalborg University, Denmark, May 2011]. https://projekter.aau.dk/projekter/files/52687369/11gr1033_ImprovingMEMS.pdf
  • [20] Yigiter, Y., & Naser, E. (2011). An optimal sensor fusion method for skew-redundant inertial measurement units. Journal of Applied Geodesy, 5(2), 99-115. https://doi.org/10.1515/jag.2011.010
  • [21] John-Olof, N., & Isaac, S. (2016). Inertial sensor arrays - A literature review. European Navigation Conference, Finland. https://doi.org/10.1109/EURONAV.2016.7530551
  • [22] Bayard, D. S., & Ploen, S. R. (2003). High accuracy inertial sensors from inexpensive components. U.S. Patent No. 20030187623A1, 2 October 2003. https://patents.google.com/patent/US6882964B2/en
  • [23] Chang, H. L., Xue, L., Qin, W., Yuan, G. M., & Yuan, W. Z. (2008). An integrated MEMS gyroscope array with higher accuracy output. Sensors, 8(4), 2886-2899. https://dx.doi.org/10.3390/s8042886
  • [24] Xue, L., Wang, L. X., Xiong, T., Jiang, C. Y., & Yuan, W. Z. (2014). Analysis of dynamic performance of a Kalman filter for combining multiple MEMS gyroscopes. Micromachines, 5(4), 1034-1050. https://doi.org/10.3390/mi5041034
  • [25] Jiang, C., Xue, L., Chang, H., Yuan, G., & Yuan, W. (2012). Signal processing of MEMS gyroscope arrays to improve accuracy using a 1st order Markov for rate signal modeling. Sensors, 12(2), 1720-1737. https://doi.org/10.3390/s120201720
  • [26] Yuan, G., Yuan, W., Xue L., Xie J., & Cha H. (2015). Dynamic performance comparison of two Kalman filters for rate signal direct modeling and differencing modeling for combining a MEMS gyroscope array to improve accuracy. Sensors, 15(11), 27590-27610. https://dx.doi.org/10.3390/s151127590
  • [27] Ji, X. (2015). Research on signal processing of MEMS gyro array. Mathematical Problems in Engineering, 2015, 120954, 1-6. https://doi.org/10.1155/2015/120954
  • [28] Tanenhaus, M. (2012). Miniature IMU/INS with optimally fused low drift MEMS gyro and accelerometers for applications in GPS-denied environments. Proceedings of the 2012 IEEE/ION Position, Location and Navigation Symposium, USA. https://doi.org/10.1109/PLANS.2012.6236890
  • [29] Richard, J. V., & Ahmed, S. Z. (2017). Reduced-drift virtual gyro from an array of low-cost gyros. Sensors, 17(2), 352-369. https://doi.org/10.3390/s17020352
  • [30] Grigorie, T. L., & Botez R. M. (2013). A redundant aircraft attitude system based on miniaturized gyro clusters data fusion. EuroCon 2013, Croatia, 1992-1999. https://doi.org/10.1109/EUROCON.2013.6625253
  • [31] Ren, Y., Ge, Y., & Bai, X. (2012). Research on optimal weight choice of multi-MEMS gyroscope data fusion. Applied Mechanics and Materials, 192, 351-355. https://doi.org/10.4028/www.scientific.net/AMM.192.351
  • [32] Zhang, W., Quan, L., & Zhang, K. (2012). Multi-layer track fusion algorithm based on supporting degree matrix. Journal of Electronics (China), 29, 229-237. https://doi.org/10.1007/s11767-012-0812-0
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-352d9de1-753b-45d5-bcd0-ba85add12dca
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