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Optimization of the Cluster-Variant Method of Constructing a Multi-Position Direction Finding System for Conditions of a Priori Uncertainty

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Abstract

The possibility of constructing a multi-position direction finding system for the case of a priori uncertainty, based on the application of the principles of multiplication of single marks of the location of the emitting target (multistructure principle) and their subsequent partition into classes (clustering principle) is considered. The criteria and algorithms for detecting the resulting cluster and for constructing the optimal estimation of target location stable to anomalous measurement errors are presented, taking into account the time costs of their computer realization. Practical recommendations and results of comparative analysis of different algorithms are given.

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REFERENCES

  1. Bulychev, Yu.G. and Golovskoi, V.A., Processing of Measurements of Angle Measuring Systems under a Priori Uncertainty in the Regularized Formulation, RE, 2010, vol. 55, no. 1, pp. 71–77.

  2. Bulychev, Yu.G. and Chepel’, Ye.N., Multistructure Method for Triangulation Estimation of Motion Parameters of a Radiating Target under a Priori Uncertainty, Teor. Sist. Upravlen., 2019, no. 6, pp. 26–42.

  3. Saibel’, A.G., Osnovy teorii tochnosti radiotekhnicheskikh metodov mestoopredeleniya (Fundamentals of the Theory of Accuracy of Radio-Technical Methods of Locating), Moscow: Oborongiz, 1958.

  4. Kukes, I.S. and Starik, M.E., Osnovy radiopelengatsii (Fundamentals of Radio Direction Finding), Moscow: Sovetskoe Radio, 1964.

  5. Teoreticheskie osnovy radiolokatsii (Theoretical Foundations of Radiolocation), Shirman, Ya.D., Ed., Moscow: Sovetskoe Radio, 1970.

    Google Scholar 

  6. Butterly, P.I., Position Finding with Empirical Prior Knowledge, IEEE Trans., 1972, vol. AES-8, no. 3, pp. 142–146.

    Google Scholar 

  7. Nunn, W.R., Position Finding with Prior Knowledge of Covariance Parameters, IEEE Trans., 1979, vol. AES-15, no. 3, pp. 204–208.

    Google Scholar 

  8. Wax, M., Position Location from Sensors with Position Uncertainty, IEEE Trans., 1983, vol. AES-19, no. 5, pp. 658–662.

    Google Scholar 

  9. Kondrat’ev, V.S., Kotov, A.F., and Markov, L.N., Mnogopozitsionnye radiotekhnicheskie sistemy (Multi-Position Radio Systems), Moscow: Radio i Svyaz’, 1986.

  10. Chernyak, V.S., Mnogopozitsionnaya radiolokatsiya (Multi-Position Radiolocation), Moscow: Radio i Svyaz’, 1993.

  11. Lin, X., Kirubarajan, T., Bar-Shalom, Y., and Maskell, S., Comparison of EKF, Pseudomeasurement and Particl Filters for a Bearing-only Target Tracking Problem, Proc. SPIE-Int. Soc. Optic. Eng., 2002, vol. 4728, pp. 240–250.

  12. Bulychev, Y.G., Bulychev, V.Yu., Ivakina, S.S., and Nasenkov, I.G., Passiv of Location of moving Targets with prior Information, Autom. Remote Control, 2017, vol. 78, no. 1, pp. 125–137.

    Article  MathSciNet  MATH  Google Scholar 

  13. Bulychev, Y.G., Bulychev, V.Yu., Ivakina, S.S., et al. Rationale for Methods of Optimal Estimation of Target Motion Parameters in Triangulation Measuring System, Teor. Sist. Upravlen., 2015, no. 4, pp. 94–110.

  14. Gustafsson, F., Particle Filters for Positioning, Navigation and Tracking, IEEE Transactions on Signal Processing, 2002, vol. 50, no. 2, pp. 425–437. https://doi.org/10.1109/78.978396

    Article  Google Scholar 

  15. Bar-Shalom, Y., Rong, Li X., and Kirubarajan, T., Estimation with Applications to Tracking and Navigation: Theory, Algorithms and Software, New York: John Wiley & Sons, 2004. https://doi.org/10.1002/0471221279

  16. Valente de Oliveira, J. and Pedrycz, W., Advances in Fuzzy Clustering and Its Applications, New York: John Wiley & Sons, 2007. https://doi.org/10.1002/9780470061190

  17. Zekavat, S. and Buehrer, R., Handbook of Position Location: Theory Practice and Advances, Hoboken, New Jersey: Wiley-IEEE Press, 2019, 2nd ed. https://doi.org/10.1002/9781119434610

  18. Zhao, J., Renzhou, G., and Xudong, D., A New Measurement Association Mapping Strategy for DOA Tracking, Digital Signal Processing, 2021, vol. 118, pp. 103–228. https://www.sciencedirect.com/science/article/pii/S1051200421002670. https://doi.org/10.1016/j.dsp.2021.103228

  19. Peng, L., Wenhui, W., Junda, Q., Congzhe, Y., and Zhenqiu, S., Robust Generalized Labeled Multi-Bernoulli Filter and Smoother for Multiple Target Tracking using Variational Bayesian, KSII Transactions on Internet and Information Systems, 2022, vol. 16, no. 3, pp. 908–928. https://doi.org/10.3837/tiis.2022.03.009

    Article  Google Scholar 

  20. Wang, X., Wang, A., Wang, D., Xiong, Y., Liang, B., and Qi, Y., A Modified Sage-Husa Adaptive Kalman Filter for State Estimation of Electric Vehicle Servo Control System, Energy Reports, 2022, vol. 8, no. 5, pp. 20–27. https://www.sciencedirect.com/science/article/pii/S2352484722003523. https://doi.org/10.1016/j.egyr.2022.02.105

  21. Widrow, B. and Stearns, S., Adaptive Signal Processing, Moscow: Radio i Svyaz’, 1989.

    MATH  Google Scholar 

  22. Granichinin, O.N. and Polyak, B.T., Randomized Estimation and Optimization Algorithms under Almost Arbitrary Disturbances, Moscow: Nauka, 2003.

    Google Scholar 

  23. Mansur, M.E. and Stepanov, O.A., Algorithms of Complex Processing in the Task of Correction of Navigation System Readings in the Presence of Nonlinear Measurements, Izv. Tulskogo Gos. Univ., Technical Sciences, 2016, no. 6, pp. 89–102.

  24. Mandel’, I.D., Klasternyi analiz (Cluster Analysis), Moscow: Finansy i Statistika, 1988.

  25. Williams, U.T. and Lans, D.N., Methods of Hierarchical Classification, Malyutov, M.B., Ed., Moscow: Nauka, 1986.

    Google Scholar 

  26. Lance, G.N. and Willams, W.T., A General Theory of Classification Sorting Strategies. 1. Hierarchical Systems, Comput. J., 1967, vol. 9, no. 4, pp. 373–380.

    Article  Google Scholar 

  27. Granichinin, O.N., Shlymov, D.S., Avros, R., and Volkovich, Z., A Randomized Algorithm for Estimating the Number of Clusters, Autom. Remote Control, 2011, vol. 72, no. 4, pp. 754–765.

    Article  MathSciNet  Google Scholar 

  28. Paklin, N.B. and Oreshkov, V.I., Cluster Silhouettes, in Sistemnyi analiz v proektirovanii i upravlenii: Sb. tr. XX Mezhdunar. nauchno-prakt. konf. (Systems Analysis in Design and Management: 20th International Scientific and Practical Conference), St. Petersburg, June 29–July 1, 2016, pp. 314–321.

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Authors and Affiliations

  1. AO “VNII “Gradient”, Rostov-on-Don, Russia

    Yu. G. Bulychev & E. N. Chepel’

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  1. Yu. G. Bulychev
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  2. E. N. Chepel’
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Correspondence to Yu. G. Bulychev or E. N. Chepel’.

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This paper was recommended for publication by O.A. Stepanov a member of the Editorial Board

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Bulychev, Y.G., Chepel’, E.N. Optimization of the Cluster-Variant Method of Constructing a Multi-Position Direction Finding System for Conditions of a Priori Uncertainty. Autom Remote Control 84, 412–423 (2023). https://doi.org/10.1134/S0005117923040045

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