A.N. Grekov, S.Y. Alekseev, V.Y. Bashkirov
Institute of Natural and Technical Systems, RF, Sevastopol, Lenin St., 28
An urgent task facing the developers of unmanned underwater vehicles (UUVs) (autonomous underwater vehicles, remotely operated underwater vehicles) is to improve the accuracy of determining the output navigation parameters: orientation angles, linear velocities and location coordinates. The systems for determining the course and attitude position (Attitude and Heading Reference Systems – AHRS) and spatial positioning systems, which are supposed to equip the UUV, should not be expensive, but technologically suitable for mass production with acceptable coordinate determination accuracy. At the first stage of the research, a prototype of the navigation platform with software for it was developed and manufactured, on which experimental data from the measuring channels were obtained in laboratory conditions, and then their preliminary processing was carried out. The analysis of existing methods for increasing the accuracy of determining the output navigation parameters of unmanned underwater vehicles showed that, despite their dynamic development and constant improvement, the error in calculating coordinates when using MEMS sensors remains high and unstable. The use of an additional hydrostatic tilt unit in the navigation platform will make it possible to compensate for the errors of MEMS sensors, which require constant correction during their stable operation, unfortunately, only for a short period of time. The analysis of typical errors of MEMS sensors is carried out. The deterministic part of the error of these sensors can be eliminated by calibration, and to estimate the stochastic errors, we used the Allan variation. The obtained error values will be used in the future to form the Kalman filter of the resulting system.
Keywords: inertial navigation system, Allan variation, MEMS, hydrostatic tilt unit.
LIST OF REFERENCES
- Groves P.D. Principles of GNSS, Inertial, and Multisensor Integrated Navigation Systems (GNSS Technology and Applications). Norwood, MA, USA: Artech House, 2008.
- Shen S.C., Chen C.J., Huang H. J., Pan C.T. Evaluation of MEMS inertial sensor module for underwater vehicle navigation application // 2010 International Conference on Mechanic Automation and Control Engineering. Wuhan, 2010. P. 3807–3810. doi: 10.1109/MACE.2010.5535510
- Ko N.Y., Choi H.T., Lee C.M., Moon Y.S. Attitude estimation using depth measurement and AHRS data for underwater vehicle navigation // OCEANS 2016 – Shanghai Shanghai, 2016. P. 1–4. doi:10.1109/OCEANSAP.2016.7485508
- Huang H., Chen X., Zhang J. Weight Self-Adjustment Adams Implicit Filtering Algorithm for Attitude Estimation Applied to Underwater Gliders // in IEEE Access, 2016. Vol. 4. P. 5695–5709. doi:10.1109/ACCESS.2016.2606408
- Hartman R., Hawkinson W., Sweeney K. Tactical underwater navigation system (TUNS) // 2008 IEEE/ION Position, Location and Navigation Symposium, Monterey, CA, 2008. P. 898–911. doi:10.1109/PLANS.2008.4570032
- Troni G., Whitcomb L.L. Experimental evaluation of a MEMS inertial measurements unit for Doppler navigation of underwater vehicles // 2012 Oceans, Hampton Roads, VA, 2012. P. 1–7. doi:10.1109/OCEANS.2012.6405003
- Zhang Q., Wan L., Pang Y. Fault-Tolerant GPS/DR Integrated Navigation System Based on Heuristic Reduction of MEMS Inertial Measurement Unit Drift // 2009 International Conference on Information Engineering and Computer Science, Wuhan, 2009. P. 1–4. doi:10.1109/ICIECS.2009.5366427
- Yuan D., Ma X., Liu Y., Zhang C. Dynamic initial alignment of the MEMS-based low-cost SINS for AUV based on unscented Kalman filter // OCEANS 2016 – Shanghai, Shanghai, 2016. P. 1–6. doi:10.1109/OCEANSAP.2016.7485669
- Krishnamurthy P., Khorrami F. A self-aligning underwater navigation system based on fusion of multiple sensors including DVL and IMU // 2013 9th Asian Control Conference (ASCC), Istanbul, 2013. P. 1–6. doi:10.1109/ASCC.2013.6606318
- Hasan A.M., Samsudin K., Ramli A.R., Azmir R.S., Ismaeel S.A. A review of navigation systems (integration and algorithms) // Australian journal of basic and applied sciences, 2009. Vol. 3(2). P. 943–959.
- Marcel Ruizenaar, Elwin van der Hall, and Martin Weiss. Gyro bias estimation using a dual instrument configuration, in Proceedings of the EuroGNC 2013, 2nd CEAS Specialist Conference on Guidance, Navigation and Control, April. 2013.
- S.J. Alekseev; A.N. Grekov; N.A. Grekov. Platform-free navigation complex with inertial orientation system built around coarse sensors and method of correction of its inertial transducers; RU2548115.
- Titterton D., Weston J. Strapdown Inertial Navigation Technology. Peter Peregrinus Ltd, 1997.
- Guerrier S. Improving accuracy with multiple sensors: Study of redundant MEMS-IMU GPS configurations, 2009.
- Pares M., Rosales J., Colomina I. Yet another IMU Simulator: Validation and Applications // Proceedings of Eurocow, 2008, Castelldefels, Spain, 2008.
- Hou H. Modeling Inertial Sensors Errors using Allan Variance // Master’s thesis, Geomatics Engineering, University of Calgary, 2004.
- El-Sheimy N., Hou H., Niu X. Analysis and Modeling of Inertial Sensors Using Allan Variance // IEEE Transactions on Instrumentation and Measurement, 2008. Vol. 57.
- Xiang Z., Gebre-Egziabher D.D. Modeling and Bounding Low Cost Inertial Sensors Errors // Proceedings of the IEEE/ION PLANS 2008, Monterey, CA, USA, 2008.
- Waegli A., Skaloud J., Tome P., Bonnaz J.-M. Assessment of the Integration Strategy between GPS and Body-Worn MEMS Sensors with Application to Sports // Proceedings of the ION GNSS 2007, Fort Worth, TX, USA, 2007.
- Pares M. On the Development of an IMU Simulator // Master’s thesis, Universitat Politecnica de Catalunya, Spain, 2008.
- Statistical Interpretation of Allan Variance as a Characteristic of Measurements and Navigation Devices, Giroskopiya i Navigatsiya, 2020, vol. 28, no. 1 (108), pp. 3–18. DOI 10.17285/0869-7035.0027.
- IEEE Std 1554™ Recommended Practice for Inertial Sensor Test Equipment Instrumentation Data Acquisition and Analysis, Dec. 2005.