Digital Signal Processing 
Russian 
Method of coding of message sources Abstract 2. Rabiner L., Gould B. Theory and application digital signal processing. M.: World, 1978. P. 848. 3. Gallager R. Theory of information and reliable communication. M.: Sovetskoe radio, 1974. 4 . Viterbi A.D., Omura D.K. Principle of digital communication and coding. M.: Radio and communication, 1982. 5. Verner M. Foundation of coding. M.: Tehnosfera, 2004. 6. Solomon D. Compression of data, image and sound. M.: Tehnosfera, 2004. 7. Slepian D. Permutation modulation. Proc. IEEE, vol. 53, Mar. 1965
Mathematical model of cognitive permutation decoder Keywords: errorcorrection coding, permutation decoding (PD), cognitive adaptation, cognitive map, a cyclic permutation, lexicographical classification, fast matrix conversion, the soft decision symbols, the energy gain from coding (EGC). References 2. Peterson W., Weldon E. errorcorrecting Codes. Per. with English. – ed. Dobrushin, R. L., and Samoylenko, S. I. – M. : Mir. – 1976. – 594 p. 3. MorelosZaragoza R. the Art of errorcorrecting coding. Methods, algorithms, application. Per. with English. – ed. Afanasyeva V. B. – M. : Technosphere. – 2005. – 320 p. 4. Sklyar B. Digital communication. Theoretical basis and practical application. – M. : Williams. – 2003. – 1104 p. 5. Gladkikh A. A. Fundamentals of the theory of soft decoding of redundant codes in the erasing communication channel. – Ulyanovsk : UlSTU, 2010. – 379 p. 6. Gladkikh A. A. Permutation decoding as a tool to improve the energy efficiency of data exchange systems / Elektrosvyaz . – ¹ 8. – 2017, P. 5256. 7. Gladkikh A. A., al Tamimi T. F. H. the Concept of cognitive data processing in the system of permutation decoding of nonbinary redundant code / Elektrosvyaz. – ¹ 9. – 2018, P. 6974.
Abstract Automatic modulation recognition is an important task on identification of different kinds of signals. In most cases, the process of signal detection and parameter recognition is held in terms of lacking priory data on functioning device capabilities. In such terms automatic signal recognition of radio source allows us to classify: transmitting device type, connection type and connection system in general. In the meantime, there are lots of ways and algorithms of digital signal identification, each appropriate for solving different tasks. Special attention is paid to works devoted to modern PSK modulation recognition methods. Algorithms highlighted in the works do not count features of shiftkeyed phase signals. Proposed algorithm allows to automatically classify signal modulation type with unknown parameters (amplitude, signal spectrum band, instant phase, rate etc…) in terms of receiving signal with PSK modulation type. Algorithm of PSK signal modulation recognition based on even degrees structure analysis, software module in math modeling space (MATLAB), practical research on noise resilience of proposed algorithm,, in which probability of correct classification is achieved with SNR > 6dB for QPSK and with SNR>7dB with DQPSK and OQPSK, and SNR>16dB for 8PSK and D8PSK signals. BPSK modulation signals are classified correctly in the SNR range >0dB. 2. Chunlei Zhang, Hui Wu, Huanyu Ning. A Novel Digital Signal Modulation Mode Recognition Algorithm. School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou, China. // Sensors & Transducers, Vol. 178, Issue 9, September 2014, pp. 194 – 198. 3. D. Le Guen, A, Mansour. Automatic Recognition Algorithm for Digital Modulated Signals / Procedings of the IASTED International Conference SIGNAL PROCESSING, PATTERN RECOGNITION & APPLICATIONS June 2528, 2002, Crete, Greece, pp. 3237. 4. Erem Erdem. Digital Modulation Recognition / A Thesis Submitted to the Graduate School of Natural and Applied Sciences of Middle East Technical University, Desember 2009. – 161 p. 5. Ivan A. Hashim, Jafar W. Abdul Sadah, Thamir R. Saeed & Jawad K. Ali. Recognition of QAM Signaks with Low SNR Using a Combined Threshold Algorithm. // IETE Journal of Research – January 2015. P.9. 6. Pedzisz M., Mansour A. Automatic modulation recognition of MPSK signals using constellation rotation and its 4th order cumulant // Digital Signal Processing 15 (2005), pp. 295304. 7. Prakasam P., Madheswaran. Modulation Identification Algorithm for Adaptive Demodulator in Software Defined Radios Using Wavelet Transform.// International Journal of Information and Communication Engineering 5:1, 2009, 7481 8. Young A.F. Classification of Digital Modulation Types in Multipath Environments, Master’s Thesis, NAVAL POSGRADUATE SCHOOL, June 2008. P. 83. 9. Baskakov S.I., Radiotehnicheskie tsepi I signaly: uchebnik dlya vuzov po spetsial’nosti “Radiotehnika” (Radio technical chains and signals: Manual for Radio technical faculties of Universities) // Fourth edition, M. Vysshaya shkola, 2003 462 p. 10. Harisov V.N., Perov A.I., Boldina V.A Global’naya sputnikovaya radionavigatsionnaya sistema GLONASS (Global sattelite radionavigation system GLONASS) // M. IPRZR 1998 – P. 400 11. Lyons R., Tsifrovaya obrabotka signalov (Signal digital processing) // Seconds edition, translation OOO “BinomPress”, 2006, 656 p. 12. Loginov A.A., Morozov O.A., Hmelev S.L. Opredelenie tipa modulyatsii fazomanipulirovannyh signalov na osnove stepennyh preobrazovaniy v analize spektra (Phase shiftkeyed modulation recognition based on power conversion and spectrum analysis) // Izvestiya vuzov. Radiofizika. Tom LV, ¹1011, 2012 13. Makarov K.S. Metody raspoznovaniya modulyatsii. (Modulation recognition methods) // Tsifrovaya obrabotka signalov ¹1, 2014. pp. 2935 14. Nagornyuk O.A., Pavlyuk V.V. Algoritm avtomatichnogo vyavleniya ta klassifikatsii signaliv z tsifrovimy vidami modulyatsii (Digital signal modulation type automatic recognition and classification algorithm) // Visnik ZDTU ¹4 (59) pp.110 15. Nevdyaev L.M. Mobil’naya svyaz’ tret’ego pokoleniya (Third generation mobile network) // Seriya izdaniy “Svyaz’I biznes”, M. MCTNI – Mezhdunarodniy centr nauchnoy i tehnicheskoy informatsii, OOO Mobil’nye kommunikatsii, 2000 – P. 208. 16. Ovchinnikov A.M., Vorobyev S.V., Sergeev S.I. Otkrytye standarty tsifrovoy trankingovoy radiosvyazy (Digital trunking radio connection open standards) // M. MCTNI, 2000 17. Solonina A.I., Ulakovich D.A., Arbuzov S.M., Solovyeva E.B. Osnovy tsifrovoy obrabotki signalov. Kurs lekciy (Signal digital processing basis) // 2 edition – SPb.: BHVPeterburg, 2005. – P. 768 18. Pavleiko M.A., Romadanov V.M. Spektral’nie preobrazovaniya v MATLAB (Spectrum conversion in MATLAB) // Spb: Nauchnoobrazovatel’niy centr “Electrofizika), 2007, P. 160 19. Rekomendatsiya MSER. (MCER recommendations) // SM.16001 (09/2012) Tehnicheskaya identifikatsiya tsifrovyh signalov, P. 30 20. Sklyar B. Tsifrovaya svyaz’ Teoreticheskie osnovy I practicheskoe primenie, 2 izdanie (Digital connecton. Theoretical basis and practical applicaton, 2nd edition) // Izdatel’skiy dom “Williams”, 2003. P. 1104 21. Stogov A.A. Tereshonok M.V., Chirov D.S., Kuz’min G.V. Raspoznovanie vidov modulyatsii radiosignalov s ispol’zovaniem momentov vysokogo poryadka (Radio signal modulation type recognition using high order moments) // TComm: Telekommunikatsiya i transport, ¹ 1, 2012, pp. 5659
Abstract It is noted that dependence of rated differential frequencies on rated time within the range of frequency modulation strongly changes at change of equivalent dielectric permeability, diameter of a wave guide and size of range of reorganization of frequency at frequency modulation. It is offered to estimate the current value of equivalent permeability, by comparison of such dependence with the reference dependence calculated at in advance preset value of reference dielectric permeability. The variation of reference dielectric permeability allows to pick up its such value at which both schedules coincide. Practical realization of the offered method is possible with use of digital processing of signals. At the same time the initial massif of counting of a signal of differential frequency breaks into a number of podmassiv, in each of which also its comparison with the same number of the reference values received at a preset value of reference dielectric permeability is made calculations of average differential frequency. The variation of value of reference dielectric permeability allows to find its such value at which there is minimum a sum of modules of differences of the received values of rated frequencies. It is offered limit simplification of this method when all massif of initial counting breaks into two podmassiv and on the average rated frequencies found for them assessment of equivalent dielectric permeability is calculated. For such calculation analytical expression is received. This expression is a basis for adaptation of the waveguide level gage to the changing value of equivalent dielectric permeability. The procedure of adaptation is iterative. The received value of dielectric permeability is used at final assessment of distance from the level gage to liquid level. For such assessment expressions for the modified integrally discrete transformation of Fourier, considering dispersive properties of a wave guide and size of equivalent permeability are received. Assessment of sensitivity of the offered method to an error of assessment of frequency and influence of noise is executed. References 2. Davydochkin V.M. Preobrazovanie Fourie v zadache izmerenia rasstoiania chastotnim dalnomerom v prostranstve s dispersiei (Fourier's transformation in a problem of measurement of distance a frequency range finder in space with dispersion) // Cifrovaja obrabotka signalov (Digital signal processing), 2015, no.1, pp. 66 – 70. 3. Spravochnik po volnovodam. Pod redakciei prof. Ja. N. Felda (Reference book on wave guides. Under edition of the prof. Ja. N. Feld. Translation from English). M “Sovetskoe radio” («Soviet radio»), 1952, 432 p. 4. Davydochkin V.M., Davydochkina S.V. Vesovie funkcii dla cifrovogo adapnivnogo garmonicheskogo analiza signalov c mnogomodovim spectrom (Weight functions for the digital adaptive harmonious analysis of signals with a multimode range) // Radiotehnika, 2009. No. 9. pp. 11 – 22. 5. Davydochkin V.M., Davydochkina S.V., Ezerski V.V. Snigenie metodicheskoi i shumovoi pogreinostei v zadache ocenki chastoti otrezka garmonicheskogo signala (Decrease in methodical and noise errors in a problem of assessment of frequency of a piece of a harmonious signal) // Radiotehnika, 2015, no. 11, pp. 66 – 71. 6. Davydochkin V.M., Ezerski V.V. Vlianie pomeh na pogreshnost radiolokacionnih urovnemerov s chastotnoi moduliaciei (Influence of hindrances on an error of radar level gages with frequency modulation) // Uspehi sovremennoj radiojelektroniki, M.: Radiotehnika. 2014, no.10, pp. 30 – 37.
Abstract Based on the calculation of the likelihood ratio and the subsequent statistical averaging of the optimal processing algorithm, principles for constructing systems for the adaptive detection of group multifrequency signals are proposed, and algorithms for group and sliding signal processing with clutter are presented. Due to the difference in Doppler phase shifts in the processed groups, the signal from a moving target falls into different Doppler channels of each of the coherent accumulators, which excludes the combination of output values of the accumulators. Overcoming the a priori uncertainty of the Doppler phase shifts of the signal is achieved by averaging the processing algorithm according to these parameters. The resulting algorithm determines the structure of the detection system, invariant to the Doppler phase shifts of the groups of the reflected multifrequency signal. In this case, the interperiod processing of each group of samples is combined, i.e., the group of samples and the subsequent incoherent summation of the results of matrix filtering fall into adaptive coherent matrix filtering. Processing is completed by summing the results of separate processing of groups of samples. A block diagram of the system for adaptive detection of a group multifrequency signal against a background of clutter is proposed. In the system, the clutter rejection is performed separately for each group of coherent pulses with a known arrival time corresponding to the tuning of the carrier frequency of the transmitter. Carrier frequency tuning in combination with adaptive group processing of incoming samples allows to significantly increase the detection efficiency of group multifrequency signals at high detection probabilities. References 2. Richards M.A., Scheer J.A., Holm W.A. (Eds.). Principles of Modern Radar: Basic Principles. New York: SciTech Publishing, IET, Edison. 2010. – 924 p. 3. Melvin W. L., Scheer J.A. (Eds.). Principles of Modern Radar: Advanced Techniques. New York: SciTech Publishing, IET, Edison, 2013. – 846 p. 4. Radar Handbook / Ed. by M.I. Skolnik. 3rd ed. McGraw–Hill, 2008. 1352 p. 5. Popov D.I. Adaptive notch filter with complex weight // Vestnik Kontserna PVO «Almaz – Antej». 2015. no 2 (14). pp. 2126. (in Russian). 6. Popov D.I. Autocompensation of the Doppler phase of clutter // Cifrovaja obrabotka signalov. 2009. no 2. pp. 30–33. (in Russian). 7. Popov D.I. Adaptive suppression of clutter // Cifrovaja obrabotka signalov. 2014. no. 4. pp. 3237. (in Russian). 8. Popov D.I. Adaptivnije regektornjie filtrij kaskadnogo tipa // Cifrovaya obrabotka signalov. 2016. no. 2. pp. 5356. (in Russian). 9. Popov D.I. Adaptive notch filter with real weights // Cifrovaya obrabotka signalov. 2017. no. 1. pp. 2226. (in Russian). 10. Popov D.I. Optimizacja nerekursivnjih regektornjie filtrov s chastichnoj adaptaciej // Cifrovaya obrabotka signalov. 2018. no. 1. pp. 2832. (in Russian). 11. Gul'shin V. A. Mnogochastotnye signaly i metody ih formirovanija i obrabotki. Vestnik Koncerna PVO «Almaz – Antej». 2013, no. 1 (9), pp. 34–40 (in Russian). 12. Popov D. I. Obnaruzhenie mnogochastotnyh radiolokacionnyh signalov. Uspehi sovremennoj radiojelektroniki. 2014, no. 6, pp. 6166 (in Russian). 13, Avagyan H., Hakhoumian A., Tovmasyan K. QAM Mapped OFDM Signal Processing on Radar Application // Telecommunications and Radio Engineering. – 2014, vol. 73, no 6, pp. 529535. 14. Zavjalov S.V., Makarov S.B., Volvenko S.V. and Balashova A.A. Efficiency of coherent detection algorithms nonortogonal multifrequency signals based on modified decision diagram // Lecture Notes in Computer Science. – 2015, vol. 9247, pp. 599–604. 15. Vertogradov G.G., Vertogradov V.G., Vertogradova E.G., Kubatko S.V. and Uryadov V.P. Drift velocity of smallscale artifical ionospheric irregularities according to a multifrequency HF doppler radar. II. Observation and modeling results // Radiophysics and Quantum Electronics. – 2015, vol. 58, no. 6, pp. 381–389. 16. Angelliaume S., Martineau P., Minchew B., Chataing S., Miegebielle V. Multifrequency Radar Imagery and Characterization of Hazardous and NOXIOUS Substances at Sea // IEEE Transactions on Geoscience and Remote Sensing. – 2017, vol. 55, no. 5, pp. 30513066. 17. Bi D., Xie Y., Ma L., Li X., Yang X., Zheng Y. R. Multifrequency Compressed Sensing for 2D Nearfield Synthetic Aperture Radar Image Reconstruction // IEEE Transactions on Instrumentation and Measurement. – 2017, vol. 66, no 4, pp. 777791. 18. Shirman Ja. D., Manzhos V. N. Teorija i tehnika obrabotki radiolokacionnoj informacii (Theory and technique of processing radar information). Moscow, Radio i svjaz', 1981, 416 p. (in Russian). 19. Popov D.I. Measurements of Characteristics of Clutter // Measurement Techniques. May 2017. Vol. 60. No 2. – P. 190195. 20. Dmitrii I. Popov and Sergey M. Smolskiy, “Estimation of the Clutter Correlation Coefficient in Radar Systems”, Infocommunications Journal, Vol. VIII, No 3, September 2016, pp. 812. 21. Patent 2660803 RF, MPK G 01 S 7/36. Filter rejection clutter / D.I. Popov, publ. 10.07.2018, bul. No. 19. 12 p. (in Russian). 22. Dmitrii I. Popov and Sergey M. Smolskiy, “Optimization of the digital rejection filter”, Infocommunications Journal, Vol. IX, No 2, June 2017, pp. 15.
Abstract Among the existing UAV autonomous navigation problem solutions the particular interest are those that are based on the comparison of the altitude profile estimating with using the radio altimeter with a digital terrain map wrote down in the UAV onboard memory. First of all, the interest is due to the possibility of obtaining highprecision estimates of the UAV's own coordinates in a wide range of flight heights, regardless of the natural surface illumination state and weather conditions at the correction sector. As a rule the UAVs’ radio altimeters antennas are characterized by small sizes and wide pattern as a consequence because of their weight and size restrictions. This results in terrain contour matching (with the sufficient sector length to compare it with a digital map of the area) is time consuming – tens of seconds or more. At the same time, broadband signal using combined with the methods of synthetic aperture can quite improve the resolution of radio altimeters and use them as terrain contour matching equipment with detail, providing a significant increase in the accuracy and efficiency of determining the UAV's own coordinates by comparing the measured altitude estimates with a digital map of the terrain. The article reveals the algorithm of detail terrain contour matching for airborne broadband radio altimeters. It shows the results of experimental test of the algorithm with data obtained using a smallsized Cband radar. Also it was found, that in addition to the terrain contour estimation, the problem of classification the probed surface can be solved. It makes it possible to determine the UAV’s own coordinates using data not only the surface heights at the correction sector, but also semantic information included with the structure of digital terrain maps. References 2. Likhachev V.P., Ryazantsev L.B. Assessment of Range and Radial Velocity of Objects of a Broadband Radar Station Under Conditions of Range Cell Migration // Measurement Techniques. February 2018, Volume 60, Issue 11, pp 1158–1162. 3. Ryazancev L.B., Kupryashkin I.F., Lihachev V.P., Gnezdilov M.V. Algoritm formirovaniya radiolokacionnyh izobrazhenij s submetrovym razresheniem v malogabaritnyh RLS s sintezirovannoj aperturoj // Cifrovaya obrabotka signalov. 2018. ¹2. S. 5358. 4. Aviacionnye sistemy radiovideniya. Monografiya / Pod red. G.S. Kondratenkova. M.: «Radiotekhnika», 2015. 648 s. 5. Ryazancev L.B., Kupryashkin I.F., Lihachev V.P. Metodika obosnovaniya trebovanij k analogocifrovomu preobrazovaniyu v RLS s sintezirovannoj aperturoj nepreryvnogo izlucheniya // Cifrovaya obrabotka signalov. 2017. ¹2. S. 4952. 6. Phalippou L., Demeestere F. Optimal retracking of SAR altimeter echoes over open ocean: Theory versus results for SIRAL2 data // https://www.aviso.altimetry.fr/fileadmin/documents/OSTST/2011/oral/01_Wednesday/Splinter%201%20IP/02%20OSTST2011PhalippouDemeestere.pdf. 7. Constant False Alarm Rate (CFAR) Detection // https://www.mathworks.com/help/phased/examples/constantfalsealarmratecfardetection.html.
The analysis of trigonometric interpolation errors in case when Dirichlet conditions are not satisfied was produced. Formulas for estimating of the mean square error of trigonometric interpolation are obtained. The mean square error of trigonometric interpolation for finitelengrth periodic discretetime signal is calculated. The results are obtained for case when interpolation time interval contains noninteger number of the signal periods. It is proved that, contrary to popular opinion, trigonometric interpolation error is not equal to zero even if the number of samples tends to infinity, but it tends to some value which depends on the parameter, one that is a product of signal duration and frequency of the recovered signal. Analytical expressions for estimation of the lower bound on mean square error for periodic finitelength discretetime signal in case of different signal duration and signal frequency are obtained. 2. Turetskiy A.Kh. Interpolation theory in problems. Minsk: Vysshaya Shkola, 1968. (in Russian). 3. Fichtenholz G. M. Fundamentals of mathematical analysis. – Ìoscow, Nauka, vol. 2, 1964 (in Russian). 4. Goncharov V.L., The theory of interpolation and approximation of functions. Ìoscow,1954 (in Russian). 5. Schmitter D., Delgado R., Unser M., Trigonometric Interpolation Kernel to Construct Deformable Shapes for UserInteractive Applications, IEEE Signal Processing Letters, Vol. 22, No. 11, November, 2015. 6. Fu D., Willson A. N., Trigonometric polynomial interpolation for timing recovery, IEEE Transactions on Circuits and Systems I: Regular Papers, Volume: 52 , Issue: 2 , Feb. 2005, pp. 338  349. 7. Petra N., Willson A. N., A HighSpeed and HighAccuracy Interpolator for Digital Modems, 2008 15th IEEE International Conference on Electronics, Circuits and Systems, 31 Aug.  3 Sept. 2008. 8. Li J., Song L., Ch. Liu, The Cubic Trigonometric Automatic Interpolation Spline, IEEE/CAA Journal of Automatica Sinica, Volume 5, Issue: 6, November 2018, pp. 1136  1141. 9. Abbas S., Irshad M., Hussain M. Z., Adaptive image interpolation technique based on cubic trigonometric Bspline representation, IET Image Process., 2018, Vol. 12 Issue 5, pp. 769  777. 10. Selva J., ConvolutionBased Trigonometric Interpolation of BandLimited Signals, IEEE Transactions on Signal Processing, Vol. 56 , No. 11 , Nov. 2008, pp. 5465  5477. 11. Porshnev S.V., Kusaykin D.V., “The accuracy of trigonometric interpolation of finitelength discretetime periodic signals”, Telecommunications and Radio Engineering, ¹7, pp. 45–50, 2017 (in Russian). 12. Porshnev S.V., Kusaykin D.V., “On features of the finitelength periodic discretetime signals reconstruction by means of trigonometric interpolation”, Journal of Instrument Engineering, ¹6, pp. 504–512, 2017 (in Russian).
Abstract The solution of the first problem is aimed at eliminating the effects of static interference (such as fog, smoke, smog, etc.), leading to a decrease in the range of visibility in video images and worsening the distinguishability of objects. In such conditions, to improve the quality of video images, it is advisable to increase the contrast. A modified CLAHE algorithm is proposed, the use of which leads to an improvement in processed images quality indexes in comparison with known algorithms: the quality of edges increases by 2 times, the standard error is reduced by 12 ... 16%, the improvement of the SSIM, PSNR, and FastFMI indices is 16 ... 18%, 5 ... 7% and 6 ... 18%, respectively, in comparison with known methods. During fusion, images coming from video cameras of different ranges are combined into a single frame containing information about all objects that are distinguishable in the image of at least one channel. This allows video surveillance at any time of the day and further improve visibility under the influence of interfering factors. When choosing the appropriate algorithm, it is advisable to use an objective quality index of multispectral image fusion, the values of which are most identical to the results of a subjective quality assessment. For comparison with the results of subjective assessments, the most widely used objective assessments of multispectral image fusion quality were selected. From the analysis of the calculated values of accuracy, monotony and inconsistency of the considered quality indicators, it follows that when choosing the algorithm for combining multispectral images for video surveillance tasks, it is advisable to use a spatial feature and a modified index of structural similarity. For these quality indicators, the accuracy value is not more than 0.68, and monotonicity is not more than 0.5. Moreover, the level of inconsistency with the subjective assessment does not exceed 6%. 2. Fisenko T.Yu., Fisenko V.T. Issledovanie i razrabotka metodov uluchsheniya podvodnyh izobrazhenij // Proceedings of X International conf. «Prikladnaya optika – 2012». 2012. Vol. 3. – pp. 294–298. 3. Jia Z., Wang H., Caballero R.E., Xiong Z., Zhao J., Finn A. A twostep approach to seethrough bad weather for surveillance video quality enhancement // Machine Vision and Applications. 2012. Vol. 23, ¹ 6. – P. 1059–1082. 4. Holopov I.S. Realizaciya algoritma formirovaniya cvetnogo izobrazheniya po signalam monohromnyh videodatchikov vidimogo i dlinnovolnovogo infrakrasnogo diapazonov v cvetovom prostranstve YCbCr // Computer optics. 2016. Vol. 40, ¹2. – pp. 266–274. 5. Gonsales R., Woods R. Cifrovaya obrabotka izobrazhenij (Digital image processing. M.: Tekhnosfera, 2012. – 1104 p. 6. Asatryan D.G. Ocenivanie stepeni razmytosti izobrazheniya putyom analiza gradientnogo polya // Computer optics. 2017. 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Objective image fusion performance measure // Electronics letters. 2000. Vol. 36, ¹ 4. – pp. 308309. 13. Haghighat M., Razian M.A. FastFMI: nonreference image fusion metric // 2014 IEEE 8th International Conference on Application of Information and Communication Technologies (AICT). IEEE. 2014. – pp. 1–3. 14. Li B., Ren W., Wang Z. RESIDE Dataset: OTS (Outdoor Training Set) URL: https://sites.google.com/view/residedehazedatasets. 01.02.2019. 15. Insarov V. V., Tihonova S. V., Mihajlov I. I. Problemy postroeniya sistem tekhnicheskogo zreniya, ispol'zuyushchih kompleksirovanie informacionnyh kanalov razlichnyh spektral'nyh diapazonov // Informacionnye tekhnologii. 2014. ¹. 3. – pp. 132. 16. Insarov V. V. et al. Formirovanie kompleksirovannyh televizionnoteplovizionnyh izobrazhenij v sistemah perednego obzora letatel'nyh apparatov // Vestnik komp'yuternyh i informacionnyh tekhnologij. 2013. ¹. 4. – Ñ. 310. 17. He C. et al. Multimodal medical image fusion based on IHS and PCA // Procedia Engineering. 2010. Vol. 7. – pp. 280285. 18. Drynkin V. N., Fal'kov E. Ya., Careva T. I. Formirovanie kombinirovannogo izobrazheniya v dvuhzonal'noj bortovoj aviacionnokosmicheskoj sisteme // Mekhanika, upravlenie i informatika. 2012. ¹. 9. – pp. 3339. 19. Rubis A. Yu., Vygolov O. V., Vizil'ter Yu. V. Morfologicheskoe kompleksirovanie izobrazhenij razlichnyh spektral'nyh diapazonov // Mekhanika, upravlenie i informatika. 2012. ¹. 8. – pp. 143148. 20. Kirillov S.N., Pokrovskij P.S., Skonnikov P.N. Algoritm kompleksirovaniya televizionnogo i infrakrasnogo izobrazhenij dlya sistem uluchshennogo videniya bespilotnyh letatel'nyh apparatov. Proceedings of conf.. «Tekhnicheskoe zrenie v sistemah upravleniya». M. 2019. – pp. 3233. 21. Orlov A. I. Ekspertnye ocenki (Expert estimations). M. 2002. – 31 p. 22. Piella G. A new quality metric for image fusion // Image Processing, 2003. ICIP 2003. 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