Synthesis of Measurement Filtering Algorithms in Navigation Systems of Unmanned Aircraft




filtering, smoothing, filter, polynomial, invariance, navigation, system, accuracy, error, noise, measurement


Unmanned aerial vehicles (UAVs) are one of the main areas of development of world aviation technology. The wide application of UAVs of various classes in both military and civilian spheres requires the development and production of high-precision on-board navigation systems of low cost, weight and dimensions. When designing navigation systems of unmanned aerial vehicles, it is assumed to use information from many sensors and correction tools, which allows to significantly increase the accuracy of the systems being developed.

To process navigational information in such systems, stochastic filtering algorithms, often the Kalman filter, and various modifications of it, which allow taking into account the nonlinear nature of the problem, are used.

Existing filtering algorithms are characterized by high computational complexity, and engineers face the problem of their practical implementation through an abstract form. That is why the work describes the method of structural synthesis of recurrent algorithms of polynomial filtering of measurements in navigation systems of unmanned aerial vehicles.

The proposed approach allows synthesizing effective polynomial filtering algorithms. At the synthesis stage, the properties of the filters are formed in terms of noise smoothing and dynamic error exclusion, and the conditions under which the digital filter will be stable are determined. The work presents an example of the synthesis of a filtering algorithm, the workability and efficiency of which is confirmed by the results of mathematical modeling.



Golembo V., Melnikov R. (2018). Organization of work for a group of drones. The Journal of Lviv Polytechnic National University "Computer Systems and Networks", Vol. 905, pp. 56-63.

Samoylenko O., Bohoslavets S., Khlopiachyi V. (2022). Main Directions of Development of Unmanned Aviation of the Armed Forces of Ukraine. Collection of scientific papers of State Research Institute of Aviation, Vol. 18, Iss. 25, pp. 218-226. DOI:10.54858/dndia.2022-18-33.

Kharchenko V. P., Chepizhenko V. I., Tunik A. A., Pavlova S. V. (2012). Avionika bezpilotnykh litalnykh aparativ [Avionics of unmanned aerial vehicles]. Kyiv: TOV «Abrys-prynt», 464 p. ISBN: 978-966-1653-05-3.

Weisong Wen, Tim Pfeifer, Xiwei Bai and Li-Ta Hsu (2021). Factor graph optimization for GNSS/INS integration: A comparison with the extended Kalman filter. NAVIGATION: Journal of the Institute of Navigation, Vol. 68, Iss. 2, pp. 315-331; DOI:10.1002/navi.421.

Ponomarenko K. V., Ryzhkov L. M. (2013). Kompleksna systema vymiriuvannia navihatsiinoi informatsii dlia systemy keruvannia polotom [A comprehensive navigation information measurement system for the flight control system]. Vostochno-Evropeiskyi zhurnal peredovykh tekhnolohyi, Vol. 6/9(66), pp. 44-55.

Maluf N., Williams K. (2004). An Introduction to Microelectromechanical Systems Engineering. Artech house, Inc, 304 p. ISBN: 1-58053-590-9.

Novatsky A. A., Kolomiitsev P. E., Sapsay P. A. (2014). Quadrator Complementary Filter With Zero Drift Temperature Compensation of the Angular Velocity Sensor. «Young Scientist», Vol. 5, Iss. 8, pp. 15-18.

Fesenko O. D. (2018). Improved method for orienting VAUs in three-dimensional space with the help of mems inertial navigation system on the basis of the Madgwick filter. Academic notes of TNU named after V.I. Vernadsky. Series: technical sciences. Aviation and rocket and space technology, Vol. 29(68), Part 1, Iss. 3, pp. 35-42.

Ghahremani N. A., Alhassan H. M. (2022). Generalized Incremental Predictive Filter for Integrated Navigation System INS/GPS in Tangent Frame. Journal of Control (English Edition), Vol. 01, No. 01, pp. 49-59. DOI: 10.52547/jocee.1.1.49.

Tsukanov O., Yakornov Y. (2022). Methods for evaluating the motion parameters of maneuvering unmanned aerial vehicles in info-communication sensor networks. Information communication and computer technologies, Vol. 2(04). DOI:1036994/2788-5518-2022-02-04-08.

Stepanov О. А. (2016). Optimal and Suboptimal Filtering in Integrated Navigation Systems. Chapter 8 In book: Aerospace Navigation Systems. Wiley, pp. 244–298. DOI:10.1002/9781119163060.ch8.

Daum, F. (2005). Nonlinear filters: beyond the Kalman filter. IEEE Aerospace and Electronic Systems Magazine, Vol. 20, Iss. 8, pp. 57–69. DOI:10.1109/MAES.2005.1499276.

Lefebvre, T., Bruyninckx, H., de Schutter, J. (2005). Nonlinear Kalman Filtering for Force-Controlled Robot Tasks. Berlin: Springer. 266 p. ISBN: 3540315047, 9783540315049.

Bar-Shalom Y., Li X., Kirubarajan T. (2001). Estimation with applications to tracking and navigation. New York, Wiley–Interscience. 581 p. ISBN: 9780471416555.

Afshari, H. H., Gadsden, S. A., Habibi, S. (2017). Gaussian filters for parameter and state estimation: A general review of theory and recent trends. Signal Processing, Vol. 135, pp. 218–238. DOI:10.1016/j.sigpro.2017.01.001.

Mao G., Drake S., Anderson B. D. O. (2007). Design of an Extended Kalman Filter for UAV Localization. Conference: Information, Decision and Control, pp. 224-229. DOI: 10.1109/IDC.2007.374554.

Meng Y., Gao S., Zhong Y., Hu G., Subic A. (2016). Covariance matching based adaptive unscented Kalman filter for direct filtering in INS/GNSS integration. Acta Astronautica, Vol. 120, pp. 171–181. DOI: 10.1016/j.actaastro.2015.12.014.

Crassidis J. L. (2006). Sigma-point Kalman filtering for integrated GPS and inertial navigation. IEEE Transactions on Aerospace and Electronic Systems, Vol. 42, Iss. 2, pp. 750-756, doi: 10.1109/TAES.2006.1642588.

Zhankue Zhao, Rong X., Li V., Jilkov P. (2004). Best Linear Unbiased Filtering with Nonlinear Measurements for Target Tracking. IEEE Transactions on aerospace and electronic systems, Vol. 40, Iss. 4, pp. 1324–1336. DOI:10.1109/TAES.2004.1386884.

Mahony R., Hamel T., Pflimlin J.-M. (2008). Nonlinear Complementary Filters on the Special Orthogonal Group. IEEE Transactions on Automatic Control, Vol. 53, Iss. 5, pp. 1203-1218, doi: 10.1109/TAC.2008.923738.

Pushkaryov Yu. A., Revenko V. B. (1995). Novyi strukturnyi metod synteza effektyvnykh tsyfrovykh fyltrov obrabotky informatsyy dlia avtomatycheskykh slediashchykh system [A new structural method for synthesizing effective digital information processing filters for automatic tracking systems]. Problemy upravlenyia i informatyky [Problems of management and computer science], Vol. 1, pp. 138-148.

Zimchuk I. V., Ishchenko V. I., Kankin I. O. (2015). Syntez alhorytmiv tsyfrovoho upravlinnia dlia avtomatychnykh slidkuvalnykh system [Synthesis of digital control algorithms for automatic tracking systems]. Systemni doslidzhennia ta informatsiini tekhnolohii [System research and information technologies], Vol. 1, pp. 30-38.

Kozheshkurt V. I., Yuzefovych V. V. (2010). Doslidzhennia skhem filtratsii alhorytmiv trasovoi obrobky informatsii v systemakh monitorynhu dynamichnykh obiektiv [Research of filtering schemes of trace information processing algorithms in dynamic object monitoring systems]. Reiestratsiia, zberihannia i obrobka danykh [Registration, storage and processing of data], Vol. 12, Iss. 4, pp. 3-12.



How to Cite

Зімчук , І. В., Шапар , Т. М. and Ковба, М. В. (2024) “Synthesis of Measurement Filtering Algorithms in Navigation Systems of Unmanned Aircraft”, Visnyk NTUU KPI Seriia - Radiotekhnika Radioaparatobuduvannia, (96), pp. 21-27. doi: 10.20535/RADAP.2024.96.21-27.



Telecommunication, navigation, radar systems, radiooptics and electroacoustics