Filtering Algorithms for Determining the Coordinates of the Object in Decision Support Systems

Authors

  • Petro Bidyuk Department of Mathematical Methods of Systems Analysis, National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute,” https://orcid.org/0000-0002-7421-3565
  • Roman Manuilenko Systems Control Theory Department Institute of Applied Mathematics and Mechanics, National Academy of sciences, Slovyansk
  • Roman Pantyeyev Aviation Computer-Integrated Systems Department, Faculty for Aeronavigation, Electronics and Telecommunication, National Aviation University, Kyiv https://orcid.org/0000-0003-4707-4608

DOI:

https://doi.org/10.18372/1990-5548.68.16089

Keywords:

parameter estimation, state estimation, dynamic system, granular filter, digital filtering, optimal filtering, non-systematic error, robot positioning, decision support system

Abstract

Methods for estimating the parameters and states of dynamical systems are an urgent task, the results of which are used in various fields, including processes in technical systems, cosmological and physical research, medical diagnostic systems, economics, finance, biotechnology, ecology and others. Despite significant scientific and practical advances in this area, researchers in many countries around the world continue to search for new methods of assessing the parameters and states of the studied objects and improving existing ones. An example of such methods is digital and optimal filtering, which have been widely used in technical systems since the middle of the last century, in particular, in the processing of financial and economic data, physical experiments and other information technologies for various purposes. The model and algorithms of granular filtering are considered on a practical example - a variant of the problem of global localization of mobile robot (global localization for mobile robots) or the problem of hijacked robot (hijacked robot problem). In the general embodiment, it is to determine the position of the robot according to the data from the sensor. This problem was generally solved by a number of probabilistic methods in the late 1990s and early 2000s. The task is important and finds application in mobile robotics and industry. The tasks of positioning submarines, aircraft, cars, etc. are essentially similar.

Author Biographies

Petro Bidyuk , Department of Mathematical Methods of Systems Analysis, National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute,”

Doctor of Engineering Science. Professor

Roman Manuilenko , Systems Control Theory Department Institute of Applied Mathematics and Mechanics, National Academy of sciences, Slovyansk

Candidate of Engineering Science. Scientific Employee

Roman Pantyeyev, Aviation Computer-Integrated Systems Department, Faculty for Aeronavigation, Electronics and Telecommunication, National Aviation University, Kyiv

Candidate of Engineering Science. Senior Lecturer

References

C. K. Chui and G. Chen, Kalman Filtering with Real-Time Applications. Berlin: Springer, 2009, 239 p.

S. Haykin, Adaptive Filtering Theory. Upper Saddle River NJ: Prentice Hall, 2007, 920 p.

S. J. Press, Subjective and Objective Bayesian Statistics. Hoboken, NJ: John Wiley & Sons, Inc., 2003, 558 p. https://doi.org/10.1002/9780470317105

Dan Liu, Zidong Wang, Yurong Liua, and Fuad E. Alsaadi, “Recursive filtering for stochastic parameter systems with measurement quantizations and packet disorders,” Applied Mathematics and Computation, Elsevier, vol. 398, 2021. https://doi.org/10.1016/j.amc.2021.125960

A. Pole, M. West, and J. Harrison, Applied Bayesian Forecasting and Time Series Analysis, Boca Raton, FL: Chapman & Hall/CRC, 2000, 410 p

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Published

2021-11-22

Issue

Vol. 2 No. 68 (2021)

Section

COMPUTER ENGINEERING

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