Criteria for selective choice of the most reliable information source in measurement systems
Authors: Radyukin A.Yu., Maslennikov A.L.
Published in issue: #5(161)/2025
Category: Aviation and Rocket-Space Engineering | Chapter: Aircraft Dynamics, Ballistics, Motion Control
The paper considers criteria for a selective option in identifying the most reliable information source on the aircraft state as a part of the measurement system. It applies to a test mathematical model of the measurement system consisting of three types of the measurement subsystems, which form a different set of the estimated elements of the aircraft state vector based on various physical principles of operation. The federal Kalman filter fuses information on the object state. Norms of the covariance matrices of error estimate formed by the local Kalman filters, as well as measurement variances of each element of the object state vector estimated separately by all the measurement subsystems, are considered as the criteria for selecting the most reliable source of information on the aircraft state. Simulation results show fundamental applicability of each of the considered criteria for selection of the most reliable source of information.
EDN JQVLVZ
References
[1] Hall D.L., Llinas J. An introduction to multisensory data fusion. Proceedings of the IEEE 85, 1997, no. 1, pp. 6–23.
[2] Zhegalov S.N., Maslennikov A.L. Primenenie metodov DBSCAN dlya klasterizatsii otmetok koordinat dinamicheskikh obyektov, formiruemykh neskolkimi istochnikami informatsii [Application of the DBSCAN method for clustering coordinate marks of dynamic objects formed by several information sources]. Aviakosmicheskoe priborostroenie — Aerospace Instrument-Making, 2025, no. 2, pp. 31–39. https://doi.org/10.25791/aviakosmos.2.2025.1461
[3] Barreto-Cubero A.J., Gómez-Espinosa A., Escobedo Cabello J.A., Cuan-Urquizo E., Cruz-Ramírez S.R. Sensor data fusion for a mobile robot using neural networks. Sensors, 2022, vol. 22, iss. 1, 305. https://doi.org/10.3390/s22010305
[4] Crisan D., Doucet A. A survey of convergence results on particle filtering methods for practitioners. IEEE Transactions on Signal Processing, 2002, vol. 50, no. 3, pp. 736–746.
[5] Welch G., Bishop G. An introduction to the Kalman filter. In Pract., 2006, vol. 7, no. 1, pp. 1–16.
[6] Stover J., Hall D., Gibson R. A fuzzy-logic architecture for autonomous multisensor data fusion. IEEE Transactions on Industrial Electronics, 1996, vol. 43, no. 3, pp. 403–410.
[7] Cui-Xia L., Wei-Ming L., Zi-Nan F. GPS/TDOA hybrid location algorithm based on federal Kalman filter. J. of Convergence Information Technology, 2010, vol. 5, no. 7, pp. 42–48.
[8] Danilenko N.V., Maslennikov A.L., Dolgova Yu.S. Selektivnyi mekhanizm resheniya zadachi orientatsii s realizatsiey na odnoplatnom kompyutere Raspberry Pi [Selective mechanism for solving the orientation problem with implementation on Raspberry Pi on-board computers]. Aviakosmicheskoe priborostroenie — Aerospace Instrument-Making, 2025, no. 1, pp. 32–43. https://doi.org/10.25791/aviakosmos.1.2025.1455
[9] Bezmen P. A. Kompleksirovanie dannykh sistemy upravleniya mobilnym robotom s ispolzovaniem rasshirennogo filtra Kalmana [Integration of mobile robot control system data using the extended Kalman filter]. Izvestiya Yugo-Zapadnogo gosudarstvennogo universiteta — Proceedings of the Southwest State University, 2019, vol. 23, no. 2, pp. 53–64. https://doi.org/10.21869/2223-1560-2019-23-2-53-64
[10] Sorenson H.W. Least-squares estimation: from Gauss to Kalman. IEEE Spectrum, July 1970, vol. 7, pp. 63–68.
[11] Tsygankova I.S., Maslennikov A.L. Kompleksirovanie navigatsionnoy informatsii GNSS po neskolkim informatsionnym istochnikami [Fusion of GNSS data from different information sources]. Journal of Advanced Research in Technical Science, 2020, iss. 19, pp. 66–70. https://doi.org/10.26160/2474-5901-2020-19-66-70
[12] Zavyalov R.A., Maslennikov A.L. O metodakh prognoza traektorii dvizheniya podvizhnogo obyekta v skheme kompleksirovaniya navigatsionnoy informatsii ot GNSS [On methods for predicting the trajectory of a moving object in the scheme of integrating navigation information from the GNSS]. In: Materialy 8-y nauchno-prakticheskoy konferentsii pamyati O.V. Uspenskogo: sbornik dokladov [Proceedings of the 8th scientific and practical conference in memory of O.V. Uspensky: collection of reports]. V.A. Sorokin, ed. Izdatelskiy Dom Akademii imeni N.E. Zhukovskogo, 2022, pp. 132–136.