Data Fusion Method in a Distributed Monitoring System
https://doi.org/10.1134/S2304487X20030062
Abstract
Modern distributed monitoring systems include observation and detection devices of different types. Heterogeneity of corresponding data creates problems associated with such data unification, with inconsistency, deficiency, and inaccuracy of data, as well as with necessity of big information volume processing. Algorithms providing heterogeneous data fusion for convenient representation of last ones, for getting reliable conclusions, and for decision making are considered in this work. The proposed approach to heterogeneous data fusion based on associate index vector application and data fusion realization on unified formal base. Start attempts of such base creation are quite justified. In this field, the approach based on transfer from original indices to generalized ones is developed. The aims of the generalized index processing methods are as follows: (i) reduction of the vector dimension, (ii) rational index nominalization, (iii) object classification without teaching, (iv) statistical analysis of classification efficiency. For the representation of indices, nominal (binary), ordinal (integer), and relative (real) variables normalized in range (0, 1) are used. It is demonstrated that the advantage of Euclidian and Manhattan metrics is the possibility of forming the diversity threshold based on the Neyman–Pearson criterion. Examples of indices of different types and their application are also presented.
About the Author
A. A. Moiseev
Research and Production Enterprise Radio Monitoring Technologies and Systems
Russian Federation
Russian Federation
141002
Mytishchi
References
1. Moiseev A. Modifikatsiya nekotorych protsedur avtomaticheskogo analiza dannych [Some procedures modification of automatic data analysis] // H & ES (Russia), 2017. vol. 9. no. 2. pp. 48–53 (in Russian).
2. Moiseev A. Otsenka parametrov nablyudeniya metodom reduktsii Evaluation of observation parameters by reduction method] // Radio industry (Russia), 2017. no. 3. pp. 19–25 (in Russian).
3. Bekeneva Ya. Preobrazovaniye dannykh ot raznorodnykh system monitoringa [Data conversion from heterogeneous systems of monitoring] // Program products and systems. 2019. vol. 32. no. 2. pp. 197–206 (in Russian).
4. Torgerson W. Theory and methods of scaling. N. Y.: Wiley, 1958. 460 p.
5. Diday E. Optimisationen classification automatique et reconnaissance des forms // Operations Research, 1972. V. 6. № V3. P. 61–95.
6. Statistical methods for digital computers. ed. by Enslein K. ea. N. Y.: Wiley, 1977. 464 p.
7. Gray M. Entropy and information theory. N. Y.: Springer, 2007. 289 p.
8. Wilks S. Mathematical statistics. NY.: Wiley, 1962. 644 p.
9. Moiseev A. Parametricheskaya diskriminatsiya [Parametrical discrimination] // Science intensive technologies (Russia), vol. 19. N 4. 2018. pp. 20–22 (in Russian).
10. Lindley D., Scott W. New Cambridge statistical tables. Cambridge: Cambridge university press. 1995. 96 p.
11. Handbook of mathematical functions.ed. by Abramovitz M.,National bureau of standards. 1964. 1046 p.
12. Empirical process techniques for dependent data. ed. by Dehling H. e. a., Boston.: Birkhauser, 2002. 383 p.
13. Sidnyaev N., Andreytseva K. Independence of the Residual Quadratic Sums in the Dispersion Equation with Noncentral χ2-Distribution // Applied Mathematics. 2011. V. 2. № 10. P. 1303–1308.
14. Siegel A. Practical business statistics. N. Y.: McGraw–Hill, 2000. 640 p.
15. Kallenberg O. Foundations of modern probability. N. Y.: Springer, 2002. 650 p.
16. Mardia K. Statistic analysis of directional data. N. Y.: Academic press.: 1972. 389 p.
17. Passega R., Byramjee R. Grain-size image of clastic deposits// Sedimentology, 1969. V. 13. № 3–4. P. 233–252.
18. Moiseev A. A. Posterior tracking of multiple target elements. Radio industry (Russia), 2020, vol. 30, no. 2, pp. 25–31 (in Russian).
19. Lancaster H. An introduction to medical statistics. N. Y.: Wiley, 1974. 305 p.
20. Scolnik M. Radar handbook. N. Y.: McGraw–Hill, 2008. 1352 p.
Review
For citations:
Moiseev A.A. Data Fusion Method in a Distributed Monitoring System. Vestnik natsional'nogo issledovatel'skogo yadernogo universiteta "MIFI". 2020;9(3):270-278. (In Russ.) https://doi.org/10.1134/S2304487X20030062
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