METHOD OF IMPROVING THE ACCURACY OF NAVIGATION MEMS DATA PROCESSING OF UAV INERTIAL NAVIGATION SYSTEM
DOI:
https://doi.org/10.15588/1607-3274-2022-3-18Keywords:
automatic control intellectual system, navigation system, unmanned aircraft vehicle.Abstract
Context. Modern theory and practice of preparation and conduct of hostilities on land, at sea, in the air, and recently in cyberspace dictates the relentless modernization of military equipment. The development of fundamentally new weapons is carried out considering one of the main requirements – maximum automation of operational processes, which allows combatants to distance themselves from each other as much as possible.
Among the newest models of armaments on the battlefield, due to the predominantly positional nature of the armed confrontation, unmanned aerial vehicles (UAVs) have become virtually indispensable due to their own multitasking. One of the ways to increase the efficiency of UAVs on the battlefield is to increase the level of technical perfection of flight control systems.
Creating new approaches to the design of unmanned aerial vehicle navigation systems, in particular, based on a platformless inertial navigation system is an urgent task that will provide automatic control of the UAV flight route in the absence of corrective signals from the global satellite navigation system.
Objective. The purpose of this work is to develop a method for improving the accuracy of MEMC navigation data processing of an inertial navigation system of an unmanned aerial vehicle based on an advanced Madgwik filter.
This method will increase the speed of data processing of navigation parameters and the accuracy of determining the positioning parameters in the space of the UAV through the use of an advanced Madgwik filter.
The paper shows the developed block diagram of MEMS PINS filtration on the basis of the improved Madgwik filter, the detailed mathematical description of filtration processes is carried out.
This method was tested experimentally in the MATLAB software environment using a real set of data collected during the flight of the UAV.
Method. To achieve this goal, the following methods were used: intelligent systems, theory of automatic control, pseudo-spectral method; methods based on genetic algorithm and fuzzy neural network apparatus.
Results. A method for improving the accuracy of MEMC navigation data processing of an inertial navigation system of an unmanned aerial vehicle based on an advanced Madgwik filter has been developed. The possibility of practical application of the obtained results and in comparison, with traditional methods is investigated. An experiment was performed in the MatLab software environment, and a comparison was made with the method of processing navigation data based on the Madgwik filter and the Kalman filter.
Conclusions. The developed method of increasing the accuracy of MEMC navigation data processing of an inertial navigation system of an unmanned aerial vehicle based on an advanced Madgwik filter shows an advantage over known methods in the absence of corrective signals from the global satellite navigation system for accuracy and speed of navigation data processing.
References
Cox Timothy H., Nagy Christopher J. , Skoog Mark A., Somers Ivan A., Ryan Warner. Civil UAV Capability Assessment [Electronic resource]. Access mode: http://www.nasa.gov/centers/dryden/pdf/111760 main_UAV_Assessment_Report_Overview.pdf.
Jaramillo C., Valenti R. G., Guo L. et al. Design and Analysis of a Single Camera Omnistereo Sensor for Quadrotor Micro Aerial Vehicles (MAVs), Sensors, 2016, Vol. 16(2), pp. 217–218. DOI: 10.3390/s16020217.
Xue L., Jiang C. Y., Wang L. X. et al. Noise Reduction of MEMS Gyroscope Based on Direct Modeling for an Angular Rate Signal, Micromachines, 2015, Vol. 6(2), pp. 266– 280. DOI: 10.3390/mi6020266.
Sheng G. R., Gao G. W., Zhang B. Y. Application of Improved Wavelet Thresholding Method and an RBF Network in the Error Compensating of an MEMS Gyroscope, Micromachines, 2019, Vol. 10, pp. 608–619. DOI: 10.3390/mi10090608.
Fakharian A., Gustafsson T., Mehrfam M. Adaptive Kalman filtering based navigation: An IMU/GPS integration approach, International Conference on Networking (ICNSC 2011), Delft, 11–13 April 2011, proceedings. Los Alamitos, IEEE, 2011, pp. 181–185. DOI: 10.1109/icnsc.2011.5874871.
Tian F., Zheng J. Y., Zhang T. Sensor fault diagnosis for an UAV control system based on a strong tracking Kalman filter, Appl. Mech. Mater, 2014, Vol. 687, pp. 270–274. DOI: 10.4028/www.scientific.net/amm.687-691.270.
Sampedro C. , Bavle H. , Sanchez-Lopez J. L. et al.] Flexible and dynamic mission planning architecture for UAV swarm coordination / [// IEEE International Conference on Unmanned Aircraft Systems, Arlington, USA, 7–10 June 2016 : proceedings. – Los Alamitos: IEEE, 2016. – P. 188 – 203. DOI: 10.1109/icuas.2016.7502669.
A complementary filter for attitude estimation of a fixedwing UAV / [M. Euston, P. Coote, R. Mahony et al.] // IEEE International Conference on Intelligent Robots and Systems, Nice, France, 22–26 September 2008 : proceedings. – Los Alamitos: IEEE, 2008. – P. 340–345. DOI: 10.1109/iros.2008.4650766.
Mahony R. Nonlinear Complementary Filters on the Special Orthogonal Group / R. Mahony, T. Hamel, J. Pflimlin // IEEE Trans. Autom. Control. – 2008, Vol. 53. – P. 1203– 1218. DOI: 10.1109/tac.2008.923738.
An extended Kalman filter for quaternion-based orientation estimation using MARG sensors / [J. L. Marins, X. Yun, E. R. Bachmann et al.] // IEEE International Conference on Intelligent Robots and Systems, Maui, HI, USA, 29 October–3 November 2001 : proceedings. – Los Alamitos: IEEE, 2002. – P. 2003–2011. DOI: 10.1109/iros.2001.976367.
Hajiyev C. Robust adaptive Kalman filter for estimation of UAV dynamics in the presence of sensor/actuator faults [Electronic resource] / C. Hajiyev, E. Soken. – Access mode: https://www.sciencedirect.com/science/article/abs/pii/ S1270963812002027?via%3Dihub. DOI: 10.1016/j.ast.2012.12.003.
Shi E. An improved real-time adaptive Kalman filter for low-cost integrated GPS/INS navigation [Electronic resource. Access mode: https://ieeexplore.ieee.org/abstract/ document/6273443. DOI: 10.1109/mic.2012.6273443.
Madgwick S.O.H., Harrison A. J. L., Vaidyanathan A. Estimation of IMU and MARG orientation using a gradient descent algorithm, IEEE International Conference on Rehabilitation Robotics, Zurich, Switzerland, 29 June–1 July 2011, proceedings. Los Alamitos, IEEE, 2011, pp. 1–7. DOI: 10.1109/icorr.2011.5975346.
Hsu Y. L., Wang J. S. Random Drift Modeling and Compensation for MEMS-Based Gyroscopes and Its Application in Handwriting Trajectory Reconstruction, IEEE Access 2019, pp. 17551–17560. DOI: 10.1109/access.2019.2895919.
Nesterov Yu. Gradient methods for minimizing composite functions. Mathematical Programming [Electronic resource]. Access mode: https://link.springer.com/article/ 10.1007/s10107-012-0629-5. DOI: 10.1007/s10107-0120629-5.
Shi G., Li X., Wang Z. A new measurement for yaw estimation of land vehicles using MARG sensors [Electronic resource]. Access mode: https://doi.org/10.1108/SR-10-20180276.
Brown A., Alken W., Macmillan P., Paniccia S. Modeling Earth’s ever-shifting magnetism [Electronic resource]. Access mode: https://doi.org/10.1029/ 2021EO153457.
Shoemaker K. Animating Rotation with Quaternion Curves, Conference of Special Interest Group on Graphics and Interactive Techniques 22–26 July 1985, proceedings, SIGGRAPH, 1985, pp. 245–254. DOI: 10.1145/325165.325242.
Shi Y. S., Gao Z. F. Study on MEMS Gyro Signal DeNoising Based on Improved Wavelet Threshold Method, Appl. Mech. Mater, 2013, Vol. (433), pp. 1558–1562. DOI: 10.4028/www.scientific.net/amr.466-467.986.
Fesenko O., Bieliakov R., Radzivilov H. et al. Trajectory Control Method Of UAV In Autonomous Flight Mode Using Neural Network MELM Algorithm, IEEE 2nd International Conference on Advanced Trends in Information Theory (ATIT), 15–18 December 2020, proceedings, IEEE, 2021, pp. 114–118. DOI: 10.1109/ATIT50783.2020.9349317.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2022 O. D. Fesenko, R. O. Bieliakov, H. D. Radzivilov, S. A. Sasin, O. V. Borysov, I. V. Borysov, T. M. Derkach, O. O. Kovalchuk
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Creative Commons Licensing Notifications in the Copyright Notices
The journal allows the authors to hold the copyright without restrictions and to retain publishing rights without restrictions.
The journal allows readers to read, download, copy, distribute, print, search, or link to the full texts of its articles.
The journal allows to reuse and remixing of its content, in accordance with a Creative Commons license СС BY -SA.
Authors who publish with this journal agree to the following terms:
-
Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License CC BY-SA that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
-
Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
-
Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.