Published January 19, 2023 | Version v1
Journal article Open

Development of a hardware emulator of a nanosatellite gyroscope

Creators

  • 1. Mohammed V University in Rabat (UM5R)

Description

The gyroscope sensor has multiple applications in consumer electronics, aircraft navigation, and control systems. Significant errors that match the corresponding data are a typical disadvantage of this sensor. This needs to be done by making error models that can be used to get the right level of measurement accuracy. For high-precision space applications, the navigation design system should take into account the angle random walk (N), bias instability error (B), and rate random walk (K) of the BMG160 gyroscope. For this reason, this paper shows how to use Allan Variance (AVAR) and Power Spectral Density (PSD) for the experimental identification and modeling of the stochastic parameters of the Bosch BMG160 gyroscope embedded in a nanosatellite in order to get an accurate gyroscope model. This work also demonstrates the principle of operation of the equivalent electronic model intended to carry out advanced simulations without recourse to the real material in order to avoid the problem of bad manipulation and availability of the material in order to reduce the time and cost of development. The interpretation of the Allan curves and the PSD obtained from the measurements collected over a long period is presented, as well as a comparison between the real raw data of the BMG160 gyroscope and the designed hardware emulator in both the time and frequency domains. This is done to evaluate the accuracy of the gyroscope model emulating the real sensor in laboratory simulations. The experimental results show that the signals from the emulator and the BMG160 gyroscope are quite close. Therefore, the proposed prototype could be an optimal solution for laboratory calculations and simulations

Files

Development of a hardware emulator of a nanosatellite gyroscope_zenodo.pdf

Files (2.1 MB)

Additional details

References

  • Pablo, H., Whittaker, G. N., Popowicz, A., Mochnacki, S. M., Kuschnig, R., Grant, C. C. et al. (2016). The BRITE Constellation Nanosatellite Mission: Testing, Commissioning, and Operations. Publications of the Astronomical Society of the Pacific, 128 (970), 125001. doi: https://doi.org/10.1088/1538-3873/128/970/125001
  • Perez, F., Modenini, D., Vázquez, A., Aguado, F., Tubío, R., Dolgos, G. et al. (2018). DustCube, a nanosatellite mission to binary asteroid 65803 Didymos as part of the ESA AIM mission. Advances in Space Research, 62 (12), 3335–3356. doi: https://doi.org/10.1016/j.asr.2018.06.019
  • Lucia, B., Denby, B., Manchester, Z., Desai, H., Ruppel, E., Colin, A. (2021). Computational Nanosatellite Constellations. GetMobile: Mobile Computing and Communications, 25 (1), 16–23. doi: https://doi.org/10.1145/3471440.3471446
  • Kirat, B. (2021). Design of nanosatellite constellations for internet of things applications. Istanbul Technical University. Available at: http://siga.uubf.itu.edu.tr/uubftez/upload/itu/uubf/uzay/Bedirhan_Kirat-uzay-20210614.pdf
  • Chiu, S.-Y., Kim, K. A., Liu, Y.-C. (2021). Analysis of Nanosatellite Impedance Interaction and Stability Based on System Operation Mode. 2021 IEEE International Future Energy Electronics Conference (IFEEC). doi: https://doi.org/10.1109/ifeec53238.2021.9662020
  • Du, J., Gerdtman, C., Lindén, M. (2018). Signal Quality Improvement Algorithms for MEMS Gyroscope-Based Human Motion Analysis Systems: A Systematic Review. Sensors, 18 (4), 1123. doi: https://doi.org/10.3390/s18041123
  • Tanenhaus, M., Geis, T., Carhoun, D., Holland, A. (2010). Accurate real time inertial navigation device by application and processing of arrays of MEMS inertial sensors. IEEE/ION Position, Location and Navigation Symposium. doi: https://doi.org/10.1109/plans.2010.5507137
  • Lefevre, H. C. (1993). The fiber-optic gyroscope. Artech Print on Demand, 332.
  • Choi, B. (1992). ARMA Model Identification. Springer New York, 200. doi: https://doi.org/10.1007/978-1-4613-9745-8
  • Ding, M., Shi, Z., Du, B., Wang, H., Han, L. (2021). A signal de-noising method for a MEMS gyroscope based on improved VMD-WTD. Measurement Science and Technology, 32 (9), 095112. doi: https://doi.org/10.1088/1361-6501/abfe33
  • Saini, V., Rana, S., Kube, M. (2010). Online estimation of state space error model for MEMS IMU. Journal of Modelling & Simulation of Systems, 1 (4), 219–225.
  • Allan, D. W. (1987). Time and Frequency (Time-Domain) Characterization, Estimation, and Prediction of Precision Clocks and Oscillators. IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control, 34 (6), 647–654. doi: https://doi.org/10.1109/t-uffc.1987.26997
  • 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. doi: https://doi.org/10.1109/tim.2007.908635
  • Tehrani, M. M. (1983). Ring Laser Gyro Data Analysis With Cluster Sampling Technique. SPIE Proceedings. doi: https://doi.org/10.1117/12.935818
  • Han, S., Meng, Z., Zhang, X., Yan, Y. (2021). Hybrid Deep Recurrent Neural Networks for Noise Reduction of MEMS-IMU with Static and Dynamic Conditions. Micromachines, 12 (2), 214. doi: https://doi.org/10.3390/mi12020214
  • Allan, D. W. (1966). Statistics of atomic frequency standards. Proceedings of the IEEE, 54 (2), 221–230. doi: https://doi.org/10.1109/proc.1966.4634
  • Curey, R. K., Ash, M. E., Thielman, L. O., Barker, C. H. (2004). Proposed IEEE inertial systems terminology standard and other inertial sensor standards. PLANS 2004. Position Location and Navigation Symposium (IEEE Cat. No.04CH37556). doi: https://doi.org/10.1109/plans.2004.1308978
  • Fossen, T. I. (2011). Handbook of Marine Craft Hydrodynamics and Motion Control. John Wiley & Sons, Inc. doi: https://doi.org/10.1002/9781119994138
  • El Fatimi, A., Addaim, A., Guennoun, Z. (2021). A low-cost IMU/GPS position accuracy experimental study using extended kalman filter data fusion in real environments. E3S Web of Conferences, 297, 01040. doi: https://doi.org/10.1051/e3sconf/202129701040
  • 952-1997. IEEE Standard Specification Format Guide and Test Procedure for Single-Axis Interferometric Fiber Optic Gyros. doi: https://doi.org/10.1109/ieeestd.1998.86153
  • Langel, S., Crespillo, O. G., Joerger, M. (2019). Bounding Sequential Estimation Errors Due to Gauss-Markov Noise with Uncertain Parameters. ION GNSS+, The International Technical Meeting of the Satellite Division of The Institute of Navigation. doi: https://doi.org/10.33012/2019.17014
  • Petkov, P., Slavov, T. (2010). Stochastic modeling of MEMS inertial sensors. Cybernetics and information technologies, 10 (2), 31–40. Available at: https://cit.iict.bas.bg/CIT_2010/v10-2/31-40.pdf
  • Addaim, A., Gretete, D., Madi, A. A. (2018). Enhanced Box-Muller method for high quality Gaussian random number generation. International Journal of Computing Science and Mathematics, 9 (3), 287. doi: https://doi.org/10.1504/ijcsm.2018.093153