Method of verification the correctness of the air object recognition process

DOI:10.31673/2412-4338.2020.022512

Abstract

The basis of the developed procedure for verification the correctness of the process of recognition of airborne objects is the method of standards. The role of the standards is fulfilled by the introduced correctness constraints. During the control, the syntactic and semantic correctness of the recognition process are verified. The method differs from the known ones in the following. When verification certain types of semantic inaccuracy of descriptions of classes of alphabets, the concept of medium risk is used. The essence of the developed method for verification the differences in the descriptions of alphabet classes is as follows. The medium risk values are calculated when each pair of alphabet classes is recognized and the implementation of the restrictions introduced is verified. Using the method allows at the stage of debugging formalized knowledge to identify inaccuracies associated with the indistinguishability of alphabet classes and use the numerical value of the medium risk to identify other types of semantic inaccuracies. The essence of the developed method for verification the structural redundancy of alphabet class descriptions is as follows. The medium risk values are calculated during class recognition using various "fragments" of its description. The restriction introduced makes it possible to reveal indistinguishable “fragments” of class descriptions. Removing redundant descriptions reduces the time it takes to find solutions for classes of objects. The developed method for identifying the characteristic redundancy of class descriptions is also based on the procedure of calculating the medium risk value, adopted as a characteristic of the a priori information content of the signs. The method allows at the stage of filling the knowledge base to automate the identification and elimination of non-informative features in class descriptions, reduces the labor required to verify redundancy. At the stage of directly solving the recognition problem, the method allows to reduce computational costs by ranking the used features by their a priori information content and the corresponding organization of the search process for the decision on recognition classes of air objects.

Keywords: data processing, features of air objects, recognition, incomplete data, decision making.

References
1. Malyarenko, A.S. (2007), “Systemi vtorychnoi radyolokatsyy dlia upravlenyia vozdushnim dvyzhenyem y hosudarstvennoho opoznavanyia” [Secondary Radar Systems for Air Traffic Control and State Recognition], HUVS, Kharkiv, 78 p. 3.
2. (2015), Specification for Surveillance Data Exchange ASTERIX Part 12 Category 21 ADS-B Target Reports, EUROCONTROL.
3. George, F. L. (2005), Artificial Intelligence: Structures and Strategies for Complex Problem-Solving. 4 ed., Williams, 864 p.
4. Yakh”yaeva, G.E. (2011), “Nechetkie mnozhestva I neironnye seti”[Fuzzy sets and neural networks], Internet-universitet informatsionnykh tekhnologii, Binom. Laboratoriya znanii, Moscow, 320 p.
5. Florov, I.B. (2011), “Informacionnye tekhnologii v radiotekhnicheskih sistemah” [Information technology in radio engineering systems], Bauman Moscow State Technical University, Moscow, 846 p.
6. Turіns'kij, O.V., Demіdov, B.O., Grib, D.A. and Khmelevs'ka, O.O. (2019), “Sistemno-konceptual'nі polozhennya j organіzacіjno-metodichnі osnovi obґruntuvannya, viboru і realіzacії obrisu perspektivnoї sistemi ozbroеnnya protipovіtryanoyi oboroni derzhavi ta yiyi zbrojnih sil” [System-conceptual provisions and organizational and methodological bases for substantiation, selection and implementation of the outline of a promising air defense system of the state and its armed forces], Systems of Arms and Military Equipment, No. 2(58), pp. 55-69. https://doi.org/10.30748/soivt.2019.58.08.
7. Chernyak, V.S. (1992), “Mnogopozicionnaya radiolokaciya” [Multipurpose radar], Radio i svyaz', Moscow, 416 p.
8. Verba, V.S. and Merkulov, V.I. (2013), “Mnogopozicionnye radiolokacionnye sistemy navedeniya. Vozmozhnosti i ogranicheniya” [Multipoint radar guidance systems. Opportunities and limitations], Radiotekhnika, Moscow, pp. 94-99.
9. Gorelik, A. L., Skripkin, V. A. (2004),”Metodyi raspoznavaniya” [Recognition methods], M.: Vyissh. Shk, 261 p.
10. Gafarov, E.R. (2016), “Graficheskii metod resheniya zadach kombinatornoi optimizatsii” [Graphic method for solving combinatorial optimization problems], Automation and Telemechanics, No. 12, pp. 26–36.
11. Zhuravlev, Yu. I. (2005), “Ob algebraicheskom podhode k resheniyu zadach raspoznavaniya ili klassifikatsii” [On the algebraic approach to solving recognition or classification problems], Problemyi kibernetiki, Vyip. 33, pp. 5–68.
12. Dempster, A.(2008), The Dempster-Shafer calculus for statisticians, IJAR, Vol. 48, pp. 365-377.
13. Dezert, J., Tchamova, A. (2011), On the behavior of Dempster’s rule of combination, School on Belief Functions Theory and Applications, Autrans, France, 4-8 April 2011 (http://hal. Archives-ouvertes. Fr/hal-00577983/).
14. Dezert, J., Tchamova, A., Dambreville, F. (2011), On the mathematical theory of evidence and Dempster’s rule of combination (http://hal. Archives-ouvertes. Fr/hal-00591633/fr/).
15. Metod vyznachennya napryamkiv udaru zasobiv povitryanogo napadu na operaty`vnomu napryamku / M.A. Pavlenko, V.K. Medvedyev, P.G. Berdnik, R.V. Safronov // Nauka i texnika Povitryany`x Sy`l Zbrojny`x Sy`l Ukrayiny`. – 2016. – # 3(24). – S. 24-27.
16. Vy`kory`stannya klitkovogo avtomatu u metodi vy`boru variantu marshrutu pol`otu udarny`x litakiv shhodo urazhennya nazemny`x cilej / Ye.S. Vorobjov, M.A. Pavlenko, Ye.Yu. Xlyebnikov, M.G. Glady`shev // Sy`stemy` ozbroyennya i vijs`kova texnika. – 2018. – # 1(53). – S. 84-90. https://doi.org/10.30748/soivt.2018.53.12.
17. Pavlenko M. A. Razrabotka protseduryi mnogoetapnoy formalizatsii znaniy dlya ekspertnyih sistem realnogo vremeni / M.A. Pavlenko // Sistemi obrobki Informatsiyi. – 2004. – # 9(37). – S. 124-133.