### Signal fusing of unequal accuracy information system based on fuzzy logic

#### Abstract

When performing integration of several not uniformally precise signals of information systems one of the main problems consists in delimitation in which it is necessary to use this or that set of signals, and a way of fusion of signals. In article the method based on assessment of values of coordinates of the center of gravity of the resulting function of accessory of an indistinct system of a conclusion is considered. For determination of value of the center of gravity the following steps are used: the type of functions of accessory of entrances and exits of a system of an indistinct conclusion is set; borders of functions of accessory of entrances and exits of an indistinct system of a conclusion are entered; for functions of accessory of entrances the rated value of parameter of quality of information systems is set; for determination of coordinate of the center of gravity I. Mamdani's algorithm is used. According to the received trajectory of the center of gravity integration borders (border of operating modes) and the weight of information signals are defined. Change of a form of functions of accessory and the fields of their crossing (borders of operating modes) allows to change a form of a trajectory of the center of gravity from rated value of a signal with big variability concerning operating conditions, specifics of work of information systems, etc. thanks to what change of nature of scales of signals of information systems is possible. An example of integration of two information systems with different levels of a mean square deviation is reviewed, and it is shown that application of the method offered in article allows to receive as a result of integration a total signal with a mean square deviation smaller, than at the most exact kompleksiruyemy information system.

#### Full Text:

PDF (Russian)#### References

Ponyatsky V. M. A way of increase in noise stability of a robotic system. Proceedings of XII international seminar "Su-percomputation and mathematical modeling" (October 11 - October 15, 2010) – Sarov, FSUE VNIIEF, 2011, P. 288-300.

Ponyatsky V. M. Fusion of results of processing and allocation of a source of useful radiation on the sequence of video images of Proc. TSU. "Radio engineering and radio optics" - Tula: TSU, 2013, V. XIII. P. 120-123.

Ponyatsky V. M. Fusion of measuring systems on the basis of Kallman filtration. Stochastic optimization in informatics. Interuniversity collection. / Under the editorship of O. N. Granichin – SPb.: Publishing house of SPb. - The St. Petersburg university, 2014. Volume 10 of the Issue 2. P. 37-41.

Ponyatsky V. M. Fusion of estimates of the coordinates of a vehicle received by different methods of processing of the sequence of video images (article) Proceedings of TSU. Technical science. Tula: TSU. 2015. Issue 2. P. 77-89/

Pogorelsky S. L., Ponyatsky V. M., Makaretsky E. A., Gublin, A. S., Ovchinnikov A. V. Increase in accuracy of measure-ment of parameters of an object on images on the basis of algorithmic fusion. Proceedings of TSU. Technical science. Issue 12. Part 2. Tula: TSU. 2016. P. 147-154

Ponyatsky V. M. Improvement of quality of the information processing arriving from several video sensors in tasks of control theory //Modern information technologies and IT education. 2016. V. 12, No. 4. P. 165-172. URL: https://elibrary.ru/item.asp?id=28151074 (date of the address: 12.07.2018).

Ponyatsky, V. M., Galangte, A. I., Egorov, D. B., Makaretsky, E. A. Selection of images of a useful source of radiation against the background of hindrances by complex criterion / Proceedings of TSU. Radio engineering and radio optics. Tula: Publish-ing house of TSU, 2013. V. XIII. P. 131 – 136.

Egorov D. B., Ponyatsky V. M., Makaretsky E. A. Allocation of the crossed trajectories of objects on the sequence of the video images. Proceedings of TSU. Technical science. 2013. No. 6-2. P. 200-205.

Pogorelsky S. L., Ponyatsky V. M., Egorov D. B., Makaretsky E. A., Ovchinnikov A. V., Gublin A. S. A complex for a re-search of processing of a video information. Proceedings of TSU. Technical science. Issue 12. Part 2. Tula: TSU. 2016, P. 135-147.

Makaretsky E. A., Ponyatsky V. M., Eremin N. N. A method of increase in efficiency of segmentation in the system of tracking traffic flows (article). Collection of materials of IX International conference “Recognition-2010”. Optical-electronic devices and devices in the systems of recognition of images, processing of images and symbolical information. – Kursk. KSTU. 2010. P. 39-41.

Ponyatsky V. M., Galangte A. I., Makaretsky E. A. Features of design of algorithms of processing of images in television measuring systems. Collection of reports MODELLING of AVIATION SYSTEMS. State scientific center of the Russian Federation of “Federal State Unitary Enterprise State Research Institute of Aviation Systems”; Russian Academy of Sciences; Russian Federal Property Fund. 2011. V. 3. P. 121-127.

Pogorelsky, S. L., Chinaryov, A. V., Semikozov, A. M. An integrated approach to improvement of images of the com-bined tele-and infra-devices / Proceedings of TSU. Technical science. Tula.: Publishing house of TSU, 2012. Issue 7. P. 291 – 296.

Shtovba, S. D. Design of fuzzy systems by MATLAB. – M.: Goryachaya Liniya – Telecom, 2007. – 288 p.

Pegat, A. Fuzzy modelling and control. – the 2nd ed. – M.: BINOMIAL. Laboratory of knowledge, 2013. – 798 p.

Leonenkov, A. V. Fuzzy modelling in the environment of MATLAB and fuzzyTECH. – SPb.: BHV-St. Petersburg, 2003. – 736 p.

Bukhalyov, V. A. Optimum smoothing in systems with accidental spasmodic structure. – M.: FIZMATLIT, 2013. – 188 p.

Fikhtengolts, G. M. Course of differential and integral calculus. In 3 v. V. 1. – the 8th ed. – M.: FIZMATLIT, 2003. – 680 p.

Buckley, J. J., Eslami, E. An Introduction to Fuzzy Logic and Fuzzy Sets. – Heidelberg; New York: Physica-Verl., 2002. – 207 p.

Cintula, P. From Fuzzy Logic to Fuzzy Mathematics. – Czech.: Czech Technical University in Prague, 2004. – 147 p.

Fuzzy Logic – Algorithms, Techniques and Implementations / Ed. by P. Dadios, – Croatia.: InTech, 2012. – 294 p.

Nguyen, H. T., Wu, B. Fundamentals of Statistics with Fuzzy Data. – Heidelberg: Springer, 2006. – 204 p.

McNeill, F. M., Thro. E. Fuzzy Logic: A Practical Approach. – London, AP PROFESSIONAL, 1994. – 309 p.

Chen, G., Pham, T. T. Introduction to Fuzzy Sets, Fuzzy Logic, and Fuzzy Control Systems. USA: CRC Press, 2001. – 329 p.

Jin, Ya. Advanced Fuzzy Systems Design and Applications. – Warsaw: Springer Physica-Verlag, 2003. – 276 p.

Buckley, J. J. Simulating Fuzzy Systems. – Warsaw: Springer, 2005. – 208 p.

Viertl, R. Statistical Methods for Fuzzy Data. – New Delhi: WILEY, 2011. – 270 p

### Refbacks

- There are currently no refbacks.

Abava FRUCT 2019

ISSN: 2307-8162