DOI 10.17586/0021-3454-2021-64-10-799-805
UDC 623.4.016
METHOD FOR TARGET OBJECT SELECTING BASED ON A LIMITED NUMBER OF MEASUREMENTS OF PHYSICALLY DISSIMILAR FEATURES
A. F. Mozhaysky Military Spaсe Academy, Department of Onboard Information and Measuring Complexes; Professor
A. K. Klyuchkin
1st State test cosmodrome of the Russian Federation Ministry of Defense; 1st Scientific Testing Department ; Engineer
A. A. Yadrenkin
A. F. Mozhaisky Military Space Academy, Department of On-board Information and Measuring Complexes; Associate Professor
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Abstract. The problem of choosing a target object from a set of objects in the observer's field of view by a limited number of measurements of dissimilar selective features, is considered. To solve the problem with a required probability for a various number of measurements of the individual features, a combined dimensionless feature is introduced. The proposed approach does not require accumulation of large volumes of measurement information and can be used to make decisions in the case of physical dissim-ilarity of the measured values.
Keywords: observed objects, dissimilar selective features, limited number of measurements, combined feature, decision making
References:
References:
- Aizerman M.A., Aleskerov F.T. Vybor variantov: osnovy teorii (Choosing Options: Theory Basics), Moscow, 1990, 240 р. (in Russ.)
- Arsen'ev V.N., Sergeev V.A., Blazhko A.K., Shostal V.Yu. Journal of Instrument Engineering, 2004, no. 12(47), pp. 3–7. (in Russ.)
- Bagrov A.V., Kirichenko D.V. Questions of Radio Electronics, 2007, no. 2(2), pp. 20–25. (in Russ.)
- Arseniev V.N., Trofimov I.A. Information and Control Systems, 2015, no. 4(77), pp. 114–118. (in Russ.)
- Amelkin S.A., Zakharov A.V., Khachumov V.M. Journal of Information Technologies and Computing Systems, 2006, no. 4, pp. 40–44. (in Russ.)
- Aivazyan S.A., Bukhstaber V.M., Enyukov I.S., Meshalkin L.D. Prikladnaya statistika: klassifikatsiya i snizheniye razmernosti (Applied Statistics: Classification and Dimension Reduction), Moscow, 1989, 607 р. (in Russ.)
- Dmitriev A.K., Yusupov R.M. Identifikatsiya i tekhnicheskaya diagnostika (Identification and Technical Diagnostics), Leningrad, 1987, 521 р. (in Russ.)
- Fomin Ya.A. Raspoznavaniye obrazov: teoriya i primeneniya (Pattern Recognition: Theory and Ap-plications), Moscow, 2012, 429 р. (in Russ.)
- Greene W.H. Econometric analysis, NY, Pearson Education, Inc., 2003, 1026 p.
- Merkov A.B. Raspoznavaniye obrazov. Postroyeniye i obucheniye veroyatnostnykh modeley (Pattern Recognition. Building and Training Probabilistic Models), Moscow, 2014, 240 р. (in Russ.)
- Arseniev V.N., Fadeev A.S. Journal of Instrument Engineering, 2013, no. 10(56), pp. 43–48. (in Russ.)
- Arseniev V.N., Petuhov A.B., Yadrenkin A.A. Journal of Instrument Engineering, 2020, no. 3(63), pp. 199–204. (in Russ.)
- Buryak Yu.I., Skrynnikov A.А. Civil Aviation High Technologies, 2015, no. 10(220), pp. 47–54. (in Russ.)
- Aleksandrovskaya L.N., Kruglov V.I., Kuznetsov A.G. et al. Teoreticheskiye osnovy ispytaniy i eksper-imental'naya otrabotka slozhnykh tekhnicheskikh sistem (Theoretical Foundations of Testing and Experimental Development of Complex Technical Systems), Moscow, 2003, 736 р. (in Russ.)
- Karmanov V.G. Matematicheskoye programmirovaniye (Mathematical Programming), Moscow, 2004, 263 р. (in Russ.)
- Pugachev V.S. Teoriya veroyatnostey i matematicheskaya statistika (Theory of Probability and Math-ematical Statistics), Moscow, 2002, 496 р. (in Russ.)