Digital
Signal Processing |
Scientific
& Technical Journal |

All the results obtained by the three methods satisfy the specified requirements for attenuation in the stopband (≥50 dB) and ISI (≤-25 dB) at close values of the coefficient wordlength differing by no more than 1 bit for each of the two values of the roll-off factor. The second and third methods in comparison with the first result in a significantly smaller number of multipliers and adders in the filter structures. The second - allows you to obtain smaller orders of filters and, therefore, the smallest group delay values. According to the level of peak-to-average power ratio of the modulated signal, the results have a spread of 4.2-5.5 dB and 7.8-9.9 dB, respectively, for roll-off factors 0.35 and 0.05. The lower limits of this parameter are achieved by the second method. How good are the obtained quantized pulse-shaping FIR filters in combination with possible additional interpolation/decimation steps in specific digital communication systems in the presence of timing jitter, noise, interference and non-linear distortions can be ascertained by mathematical and/or physical modeling. 2. Assalini A., Tonello A. M. Improved Nyquist pulses// IEEE Commun. Letters, 2004, vol. 8, no. 2, pp. 87-89. 3. Bobula M., Prokes A., Danek K. Nyquist filters with alternative balance between time- and frequency-domain parameters// EURASIP J. Adv. in Signal Processing, vol. 2010, Article ID 903980, 11p. 4. Assimonis S. D., Matthaiou M., Karagiannidis G. K., Nossek J. A. Improved parametric families of intersymbol interference-free Nyquist pulses using inner and outer functions// IET Signal Processing, 2011, vol. 5, no. 2, pp. 157–163. 5. Samueli H. On the design of optimal equiripple FIR digital filters for data transmission applications// IEEE Trans. on CAS, 1988, vol. 35, no. 12, pp. 1542-1546. 6. Ramachandran R. P., Kabal P. Minimax design of factorable Nyquist filters for data transmission systems// Signal Processing, 1989, vol. 18, no. 3, pp. 327-339. 7. Samueli H. On the design of FIR digital data transmission filters with arbitrary magnitude specifications//IEEE Trans. on CAS, 1991, vol. 38, no. 12, pp. 1563-1567. 8. Farhang-Boroujeny B., Mathew G. Nyquist filters with robust performance against timing jitter//IEEE Trans. on SP, 1998, vol. 46, no. 12, pp. 3427-3431. 9. Ashrafi A., Harris F. J. A novel square-root Nyquist filter design with prescribed ISI energy// Signal Processing, 2013, vol. 93, no. 9, pp. 2626-2635. 10. Siohan P., Moreau de Saint-Martin F. New designs of linear-phase transmitter and receiver filters for digital transmission systems// IEEE Trans. on CAS-II.1999, vol. 46, no. 4, pp. 428-433. 11. Boonyanant P., Tantaratana S. Design and hybrid realization of FIR Nyquist filters with quantized coefficients and low sensitivity to timing jitter// IEEE Trans. on SP, 2005, vol. 53, no. 1, pp. 208–221. 12. Farhang-Boroujeny B. A square-root Nyquist (M) filter design for digital communication systems// IEEE Trans on SP, 2008, vol. 56, no. 5, pp. 2127-2132. 13. Yao C.-Y., Willson A. N. The design of hybrid symmetric-FIR/analog pulse-shaping filters// IEEE Trans. on SP, 2012, vol. 60, no. 4, pp. 2060-2065. 14. Ashrafi A. Optimized linear phase square-root Nyquist FIR filters for CDMA IS-95 and UMTS standards// Signal Processing, 2013, vol. 93, no. 4, pp. 866-873. 15. Traverso S. A family of square-root Nyquist filter with low group delay and high stopband attenuation// IEEE Commun. Letters, 2016, vol. 20, no. 6, pp. 1136-1139. 16. Yao C.-Y., Wang S.-C. A QCQP design method of the symmetric pulse-shaping filters against receiver timing jitter// ISCAS, 2017, 4p. 17. Xiao R., Lei Q., Guo X., Du W., Zhao Y. A design of two sub-stage square-root Nyquist matched filter// IEEE Access, 2018, vol. 6, may, pp. 23292-23302. 18. Renfors M., Saramaki T., Pulse-shaping filters for digital transmission systems// GLOBECOM, 1992, pp. 467-471. 19. Vaisanen K., Renfors M. Efficient digital filters for pulse-shaping and jitter-free frequency error detection and timing recovery// Signal Processing, 2001, vol. 81, no. 4, pp. 829-844. 20. Samueli H. The design of multiplierless digital data transmission filters with powers-of-two coefficients// Proc. IEEE Int. Telecomm. Symp., 1990, pp. 425-429. 21. Kim H. Computer simulation results and analysis for a root-raised cosine filter design using canonical signed digits// NASA Technical Memorandum 107327, 1996, 16p. 22. Bonnaud A., Feltrin E., Barbiero L. DVB-S2 extension: end-to-end impact of sharper roll-off factor over satellite link// SPACOMM, 2014, pp. 36-41. 23. Lim Y. C., Yu Y. J. A width-recursive depth-first tree search approach for the design of discrete coefficient perfect reconstruction lattice filter bank// IEEE Trans. on CAS: II, 2003, vol. 50, no. 6, pp. 257-266. 24. Yli-Kaakinen J., Saramaki T., Bregovic R. An algorithm for the design of multiplierless two-channel perfect reconstruction orthogonal lattice filter banks// ISCCSP, 2004, pp. 415-418. 25. Mingazin A. T. Two examples of multiplierless perfect reconstruction lattice filter bank design //11-th Int. Conf. Digital Signal Processing and its Applications (DSPA-2009), vol.1, pp. 99-102. 26. Vaidyanathan P. P. Multirate digital filters, filter banks, polyphase networks, and applications: a tutorial //Proceedings of the IEEE, 1990, vol. 78, no 1. pp. 56-93. 27. Mingazin A. T. Variation of initial parameters in design FIR digital filters with finite wordlength coefficients// 3-th Int. Conf. Digital Signal Processing and its Applications (DSPA-2000), vol.1, pp. 162-166. 28. Mingazin A. T. Variation of initial parameters of weighted Chebyshev approximation in multiplierless FIR filter design (DSPA-2005)//7-th Int. Conf. Digital Signal Processing and its Applications (DSPA-2005), vol. 1, pp. 54-56. 29. Mingazin A. T. Three-dimensional graphics in analysis problem of quantized FIR filters// Digital Signal processing. Russian Scientific and Technical Journal, 2020, no. 2, pp. 46-51.
-mail:ekuzmin@sfu-kras.ru Siberian Federal University (SibFU), Russia, Krasnoyarsk
2. Davidovici S., Kanterakis E.G. Narrow-Band Interference Rejection Using Real-Time Fourier Transforms // IEEE Transactions on Communications, Jul. 1989. V.37. ¹7. pp.713–722. 3. Adaptivnaja obrabotka signalov (Adaptive signal processing) / B. Widrow, S. Stearns. M.: Radio i svjaz'. 1989. 440 p. 4. Cifrovaja chastotnaja selekcija signalov (Digital frequency selection of signals) / V.V. Vit-yazev. M.: Radio i svjaz', 1993. 240 p. 5. Shilov A.I., Bakit'ko R.V., Pol'shhikov V.P., Hackelevich Ja.D. Predvaritel'naja obrabotka shumopodobnyh signalov pri nalichii sil'nyh interferencionnyh pomeh (Preprocessing of spread spectrum signals in the presence of strong interference interference) // Radiotehnika. 2005. no 7. pp. 31–35. 6. Perov A.I. Sintez optimal'nogo algoritma obrabotki signalov v prijomnike sputnikovoj nav-igacii pri vozdejstvii garmonicheskoj pomehi (Synthesis of an optimal signal processing algorithm in a satellite navigation receiver under the influence of harmonic interference) // Radiotehnika. 2005. no 7. pp. 36–42. 7. Bakit'ko R.V., Pol'shhikov V.P., Shilov A.I., Hackelevich Ja.D., Boldenkov E.N. Ispol'zovanie vesovyh funkcij dlja predvaritel'noj obrabotki shumopodobnyh signalov pri nalichii sil'nyh interferencionnyh pomeh (Using weighting functions for preprocessing spread spectrum sig-nals in the presence of strong interference) // Radiotehnika. 2006. no 6. pp. 13–17. 8. Perov A.I., Boldenkov E.N. Issledovanie adaptivnyh transversal'nyh fil'trov dlja prijomni-kov sputnikovoj navigacii pri vozdejstvii uzkopolosnyh pomeh (Investigation of adaptive transversal filters for satellite navigation receivers under the influence of narrow-band interference) // Radio-tehnika. 2006. no 7. pp. 98–105. 9. GLONASS. Printsipy postroeniya i funktsionirovaniya (GLONASS. Design Principles and Functioning) / ed. by A.I. Perov, V.N. Kharisov. Ì.: Radiotekhnika. 2010. 800 p. 10. Kuzmin E.V., Zograf F.G. Povyshenie verojatnosti pravil'nogo poiska shumopodobnogo signala po vremeni zapazdyvanija na fone tonal'noj pomehi (Enhancement of the probability of spread-spectrum signal correct searching in case of narrow-band interference) // Uspekhi sovremen-noi radioelektroniki (Achievements of Modern Radioelectronics). 2016. no 11. pp. 137–140. 11. Kulikov G.V., Nesterov A.V., Leljuh A.A. Pomehoustojchivost' priema signalov s kvadraturnoj amplitudnoj manipuljaciej v prisutstvii garmonicheskoj pomehi (Noise immunity of re-ceiving signals with quadrature amplitude shift keying in the presence of harmonic interference) // Zhurnal radiojelektroniki [jelektronnyj zhurnal]. 2018. no 11. URL: http://jre.cplire.ru/jre/nov18/9/text.pdf. 12. Kuzmin E.V. O vlijanii kvantovanija po urovnju na jeffektivnost' procedury poiska shu-mopodobnogo signala po zaderzhke na fone shuma i garmonicheskoj pomehi (Efficiency of the spread spectrum signal searching procedure in case of continuous wave interference and quantiza-tion effect) // Cifrovaja obrabotka signalov (Digital signal processing). 2020. no 2. pp. 41–45. 13. Shahtarin B.I. Analiz fazovoj avtopodstrojki pri vozdejstvii garmonicheskoj pomehi i shuma (Phase-locked analysis for harmonic interference and noise) // Radiotehnika i jelektronika. 2021. V. 66. no 8. pp. 782–790. 14. Kuzmin E.V. Analiz chastotnyh harakteristik procedur kvadraturnoj korreljacionnoj obrabotki kompleksnyh signalov (Analysis of the frequency responses of the quadrature correlation processing of complex signals) // Cifrovaja obrabotka signalov (Digital signal processing). 2020. no 4. pp. 13–20. 15. Harris F.J. On the Use of Windows for Harmonic Analysis with the Discrete Fourier Transform // Proceedings of the IEEE, Jan. 1978. V.66. pp.51–83. 16. Cifrovoj spektral'nyj analiz i ego prilozhenija (Digital Spectrum Analysis and its Applica-tions) / S.L. Marpl.-ml. Per. s angl. M.: Mir. 1990. 584 p. 17. Metody i tehnika obrabotki signalov pri fizicheskih izmerenijah: v 2-h tomah (Methods and techniques for signal processing in physical measurements: in 2 volumes) / Zh. Maks. M.: Mir, 1983. V. 1. 312 p. 18. Okonnye funkcii dlja garmonicheskogo analiza signalov (Window functions for harmon-ic analysis of signal) / V.P. Dvorkovich, A.V. Dvorkovich. M.: Tehnosfera, 2016. 208 p. 19. Cifrovye radiopriemnye sistemy: Spravochnik (Digital Radio Receiving Systems: A Handbook) / ed. by M.I. Zhodzishskii. M.: Radio i svjaz', 1990. 208 p. 20. Kuzmin E.V. Issledovanie jeffektivnosti besporogovoj procedury poiska psevdoslu-chajnogo signala pri ogranichenii razrjadnosti vhodnyh nabljudenij (Efficiency of the non-threshold spread spectrum signal searching procedure in case of quantization of the incoming observations) // Cifrovaja obrabotka signalov (Digital signal processing). 2020. no 1. pp. 9–12.
Possible energy losses of communication systems with a PC as compared to the Shannon limit are introduced. It is demonstrated that these losses can be insignificant only for low-speed communication systems. For high-speed communication systems, they turn out to be rather material, especially when using a PC with a low code speed. It is noted that in promising communication systems intended for the transmission of messages with high speed and high energy efficiency, it is advisable to use multidimensional AS that are optimal according to Shannon, which make it possible to ensure high reliability of message reception without the application of error-correcting codes. 2. John G. Proakis Digital Communications. McGraw-Hill Publishing Company, 1989, p. 608. 3. Georg C. Clark, Jr., Bibb J. Cain Error – Correcting Coding for Digital Communication. Springer, Boston, New York, 1981, P. 422 4. Chase D. A class of algorithms for decoding block codes with channel measurement information, IEEE Trans. Inf. Theory, vol. IT-18, Jan. ¹ 1, 1972, pp. 170–182 5. Peterson W. Wesley, Weldon E.J. Error –Correcting Codes. The M.I.T. Press, Cambridge, Second Edition, 1972, P. 576 6. Uryvsky L., Osypchuk S. The analytical description of regular LDPC codes correcting ability. Transport and Telecommunication, vol.15, ¹ 3, 2014, pp. 177–184 7. A.A. Frolov, V.V. Zyablov, Granitsy minimal'nogo kodovogo rasstoyaniya dlya nedvoichnykh kodov na dvudol'nykh grafakh, Problemy peredachi informatsii, (Boundaries of the minimum code distance for nonbinary codes on bipartite graphs, Problems of Information Transmission), vypusk 4, 2011, pp. 27–42 8. Bykhovskiy M.A. Giperfazovaya modulyatsiya – optimal'nyy metod peredachi soobshcheniy v gaussovskikh kanalakh svyazi. (Hyperphase modulation is the optimal method for transmitting messages in Gaussian communication channels) M.: Tekhnosfera, 2018, P. 310 9. Vicente Torres 1, Javier Valls 1, Maria Jose Canet and Francisco Garcia-Herrero, Soft-Decision, Low-Complexity Chase Decoders for the RS (255,239) Code. Electronics, 2019, pp. 8-10 10. Yingquan Wu, Fast Chase Decoding Algorithms and Architectures for Reed–Solomon Codes, IEEE Trans. Inf. Theory. Vol. 58, January ¹ 1, 2012, pp. 109-129 11. Siyun Tang and Xiao Ma Member, IEEE A New Chase-type Soft-decision Decoding Algorithm for Reed-Solomon Codes. Electronics and Communication Engineering, Sun Yatsen University, 2013, pp. 1-28 12. Nemirovskiy E.E., Romanenko G.V., Mikhaylovskaya L.G. Analiticheskiye otsenki kvazioptimal'nykh metodov priyema v tselom blochnykh kodov. Problemy peredachi informatsii, vyp. 4, 1981, str. 34-40. (Nemirovsky E.E., Romanenko G.V., Mikhailovskaya L.G. Analytical estimates of quasi-optimal reception methods for block codes in general. Problems of Information Transmission, vol. 4, 1981, pp. 34-40) 13. G. David Forney Concatenated Codes. Research Monograph (¹ 37), M.I.T Press, 1966, P.176 14. Berrou Ñ., Glavieux A., Thitimajshima P. Near Shannon limit error-correcting coding and decoding: Turbo-codes. Proc. IEEE Int. Conf. Communications, Geneva, Switzerland, 1993, pp. 1064-1070 15. MacKay D.J.C., Neal R.M. Near Shannon limit performance of low density parity check codes. Electronics Letters, 13th March, Vol. 33, ¹ 6, 1997, pp. 1645 – 1646 16. Shannon, C. E. Probability of error for optimal codes in a Gaussian channel. the Bell System Technical Journal, vol. 38, May ¹ 3, 1959, pp. 611–656
Thus, the article is devoted to the development of an algorithm that provides up to two... three times declined the computational costs of the on-board computer when generating radar imag-es by excluding image elements from the calculation that are not included in the main beam of the antenna pattern. The calculation of the elements is carried out taking into account the presence of trajectory instabilities in the angle of demolition caused by a crosswind during the flight of a small-sized unmanned aerial vehicle. The leeway is determined based on an estimate of the average Dop-pler frequency in the signal at the output of the radar receiver. The results of the algorithm study showed that the proposed algorithm provides a two- to three-fold reduction in the time spent on the formation of a radar image in the presence of trajectory flight instability along the drift angle. Moreover, the greater the value of the drift angle– the greater the gain in the time of formation of the radar image is observed. This is due to an increase in the size of the frame at large values of the drift angle. Along with reducing the time of radar image for-mation, the use of geometric correction procedures makes it possible to improve the quality of au-tomatic focusing algorithms, simplify the procedure for further georeferencing images to digital ter-rain maps, and also improve the perception of images by the decoder operator. 2. Kupryashkin I.F., Lihachev V.P., Ryazancev L.B. Kratkij opyt sozdaniya i pervye rezul'taty prakticheskoj s"emki poverhnosti malogabaritnoj RLS s sintezirovaniem apertury antenny s borta mul'tikoptera. (A brief experience of creating and the first results of practical shooting of the surface of a small-sized radar with synthesizing the antenna aperture from the side of a multicopter). ZHur-nal radioelektroniki [elektronnyj zhurnal], 2019. ¹ 4. Rezhim dostupa: http://jre.cplire.ru/jre/apr19/12/text.pdf. 3. Dmitriev A.V., ZHarkov D.S., YArcev I.M., Polovinkina A.S. Maket malogabaritnoj pro-grammno-opredelyaemoj RLS s sintezirovaniem apertury antenny na mul'tikoptere (Layout of a small-sized software-defined radar with synthesizing the antenna aperture on a multicopter) // Sbornik trudov XXV Mezhdunarodnoj nauchno-tekhnicheskoj konferencii, posvyashchennoj 160-letiyu so dnya rozhdeniya A.S. Popova. Voronezh: VGU, 2019. S. 164-180. 4. Brajtkrajc S.G., Il'in E.M., Polubekhin A.I., Prishchep D.V., YUrin A.D., Homyakov K.A. Problemy i puti sozdaniya radiolokacionnyh sistem dlya bespilotnyh letatel'nyh apparatov taktich-eskogo i operativno-takticheskogo naznacheniya (Problems and ways of creating radar systems for unmanned aerial vehicles for tactical and operational-tactical purposes) // Izvestiya Tul'skogo gosu-darstvennogo universiteta. Tula: TGU, 2018. S. 303-313. 5. Gnezdilov M.V., Kupryashkin I.F., Lihachev V.P., Ryazancev L.B. Algoritm formirovaniya radiolokacionnyh izobrazhenij s submetrovym razresheniem v malogabaritnyh RLS s sintezirovannoj aperturoj (Algorithm for generating radar images with a submeter resolution in small-sized synthetic aperture radars) // Cifrovaya obrabotka signalov,2018. ¹ 2. S. 53-58. 6. Gulyaev G.A., Ivannikova M.V., Ryazancev L.B., Unkovskij A.V. Issledovanie vliyaniya traektornyh nestabil'nostej poleta nositelya malogabaritnoj RLS s sintezirovannoj aperturoj na kachestvo formiruemyh radiolokacionnyh izobrazhenij (Investigation of the influence of trajectory instabilities of the flight of a carrier of a small-sized radar with a synthesized aperture on the quality of the generated radar images ) // Cifrovaya obrabotka signalov, 2021. ¹ 2. S. 25-31. 7. Aviacionnye sistemy radiovideniya. Monografiya (Aviation radio vision systems) / Pod red. G.S. Kondratenkova. M.: Radiotekhnika, 2015. c. 648. 8. Kolchinskij V.E., Mandurovskij I.A., Konstantinovskij M.I. Avtonomnye doplerovskie ustrojstva i sistemy navigacii letatel'nyh apparatov (Autonomous Doppler devices and aircraft navi-gation systems) / Pod red. V.E. Kolchinskogo. M.: Sov. radio, 1975. 432 s. 9. Ryazancev L.B. Mnogomodel'noe bajesovskoe ocenivanie vektora sostoyaniya manevrennoj vozdushnoj celi v diskretnom vremeni (Multimodel Bayesian estimation of the state vector of a ma-neuverable aerial target in discrete time) // Vestnik TGTU, 2009. ¹ 4. S. 729-739.
Therefore, it is of interest to obtain an extended (functional) relationship of each component of the gradients with respect to all other components for the possibility of more flexible contour se-lection. Taking into account the presence of gradients of each spectral component of the GSI, which in themselves are a spatial characteristic of brightness differences, we will consider the correlation and structural functions as their functional relationship. The efficiency of correlation and structural functions in the task of selecting the contours of spectral-selective objects on hyperspectral images has been studied. The results obtained indicate a higher noise immunity and informativeness of the structural function compared to the correlation function. An approach to the synthesis of an optimal algorithm for the selection of contours of spec-tral-selective objects based on the distribution densities of the values of the structural function of spectral gradient images is proposed.
2. Pozhar V.E., Balashov A.A., Bulatov M.F. Modern spectral optical devices of STC UP RAS // Scientific instrumentation. 2018. Vol. 28. No. 4. pp. 49-57. 3. Gonzalez R., Woods R. Digital image processing. Moscow: Technosphere, 2019. 1104 p. 4. Kim N.V. Image processing and analysis in technical vision systems: Textbook. M. Pub-lishing House MAI, 2014. 144 p. 5. Image processing in aviation vision systems / Edited by L.N. Kostyashkin, M.B. Nikifo-rov. M.: Fizmatlit, 2016. 240 p. 6. Antonushkina S.V., Eremeev V.V., Makarenkov A.A., Moskovitin A.E. Peculiarities of analysis and processing of information from hyperspectral survey systems of the Earth's surface / Digital signal processing. 2010. No. 4. pp. 38-43 7. Modern technologies for processing Earth remote sensing data / Edited by V.V. Eremeev. M.: Fizmatlit, 2015. 460 p. 8. Sheremetyeva T.A., Filippov G.N., Malov A.M. Application of the target visualization method for hyperspectral image processing // Optical Journal. 2015. Vol. 82. No. 1. pp. 32-36. 9. Rytov S.M. Introduction to statistical radiophysics. Part 1. Random processes M.: Nauka. 1976. 496 p. 10. Prokhorov S.A., Grafkin V.V. Structural and spectral analysis of random processes. Sa-mara: SNC RAS, 2010. 128 p. 11. Levin B.R. Theoretical foundations of statistical radio engineering. Moscow: Sovetskoe radio, 1968. 504 p.
Kupryashkin I.F., e-mail: ifk78@mail.ru Mazin A.S., e-mail: mazinant@rambler.ru Military Educational and Scientific Center of the Air Force “N.E.Zhukovsky and Y.A.Gagarin Air Force Academy”, Russia, Voronezh Keywords:
The article describes the results of a study on the efficiency of object marks classification by a deep convolutional neural network under the intentional radar interference influence. The article deals with an algorithm of training data preparation procedure, a description of input images (pattern), providing maximum activation of convolution layer filters at different interference (noise) intensities, as well as evaluation of classification accuracy of ground object marks on radar images at different interference/signal ratios on training and test sets The procedure of training data preparation includes elimination of surface marks (terrain back-ground) on the MSTAR radar images set, decreasing the size of images to the objects size and addition of image noise. From comparison of the received input images (patterns), providing maximum activation of the convolutional network filters, trained on a set of images without interference and on a set with interference/signal ratio q=0 dB, it is clear that under the interference influence the texture diversity of the features in higher layers became much smaller, and the patterns themselves - more homogeneous. This fact proves the decrease of the network sensitivity to the classification features of a particular set of images under the influence of interference. The influence of interference is quite expectedly manifested in a decrease in the accuracy of classification of object marks on the radar images. The maximum accuracy value in an interference-free condition is 97.91%, at an interference level comparable to the average level of object marks (q = 0 dB) it remains quite high - 86.13%, but with a further increase in the signal-to-noise ratio it decreases rapidly. For example, if the q = 5 dB a correct network operation is seen in about a half of cases (55,01 %), and if the q = 15 dB and more - 13,18 %, that practically comes down to a simple guess (for the ten-alternative classification the accuracy is about 10 %).
2. Wang H., Chen S., Xu F., Jin Y.-Q. Application of Deep-Learning Algorithms to MSTAR Data. 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2015, pp. 3743-3745. DOI: 10.1109/IGARSS. 2015.7326637. 3. Chen S., Wang H., Xu F., Jin Y.-Q. Target Classification Using the Deep Convolutional Networks for SAR Images. IEEE Transaction Geoscience and Remote Sensing, 2016, vol. 54, no. 8, pp. 4806-4817. DOI: 10.1109/TGRS. 2016.2551720. 4. Anas H., Majdoulayne H., Chaimae A., Nabil S.M. Deep Learning for SAR Image Classification. 2020. DOI: 10.1007/978-3-030-29516-5_67. 5. Chen S., Wang H. SAR Target Recognition Based on Deep Learning. 2014 International Conference on Data Science and Advanced Analytics (DSAA), 2014, pp. 541-547. DOI: 10.1109/DSAA.2014.7058124. 6. Coman C., Thaens R. A Deep Learning SAR Target Classification Experiment on MSTAR Dataset. 2018 19th International Radar Symposium (IRS), 2018, pp. 1-6. DOI: 10.23919/IRS.2018.8448048. 7. Furukawa H. Deep Learning for End-to-End Automatic Target Recognition from Syn-thetic Aperture Radar Imagery. arXiv:1801.08558v1 [cs.CV] 25 Jan 2018. 8. Profeta A., Rodriguez A., Clouse H.S. Convolutional Neural Networks for Synthetic Ap-erture Radar Classification. Proc. SPIE 9843, Algorithms for Synthetic Aperture Radar Imagery XXIII, 98430M (14 May 2016). https://doi.org/10.1117/12.2225934. 9. Borodinov A.A., Myasnikov V.V. Comparison of radar image classification algorithms for various preprocessing methods based on MSTAR data. IV International Conference and Youth School "Information Technologies and Nanotechnologies" (ITNT-2018). 10. Zhang C., Li P., Sun G., Guan Y., Xiao B., Cong J. Optimizing FPGA-based accelerator design for deep convolutional neural networks. Proceedings of the 2015 ACM/SIGDA Internation-al Symposium on Field Programmable Gate Arrays, 2015.pp. 161-170. DOI: 10.1145/2684746.2689060. 11. I.V. Zoev, N.G. Markov, A.P. Beresnev, T.A. Yagunov. FPGA-based hardware imple-mentation of convolution neural networks for images recognition. GraphiCon, 2018. pp. 200-203. 12. Web-site www.sdms.afrl.af.mil/index.php?collection=mstar. 13. Chollet F., "Deep Learning with Python". Saint Petersburg: Piter, 2018. 400 p.
Okhotnikov S.A., e-mail: OhotnikovSA@volgatech.net Khafizov D.G., e-mail: HafizovDG@volgatech.net Egoshina I.L., e-mail: EgoshinaIL@volgatech.net Khafizov R.G.,, e-mail: HafizovRG@volgatech.net The Volga State Technological University (VSUT, Volgatech), Russia, Yoshkar-Ola Keywords: rediscretization, equalization, spectrum shape consistency, alignment of the contour dimensions.
When evaluating resampling methods, continuous complex-valued contours on the complex plane were specified as reference images, followed by their discretization. In the next step the dimension alignment procedure was performed. The normalized scalar product of the contours was used to determine the similarity measure. Simulation results show that the method of maximum preservation of the contour spectrum realized by the scheme "interpolation - filtering - thinning" provides the value of the normalized scalar product of the contours close to the value of the normalized scalar product of the original continuous contours. In addition, the value of the normalized scalar product of contours at decreasing the dimensionality is higher than at oversampling with increasing the dimensionality.
2. Karim S. A. A. Rational Bi-Quartic Spline with Six Parameters for Surface Interpolation with Application in Image Enlargement // IEEE Access, 2020. – Vol. 8. – PP. 115621–115633. 3. Pyavchenko A.O., Petrenko E.V. Analiz i vybor cifrovyh fil'trov dlya perediskretizacii kadrov cifrovogo izobrazheniya dlya rezhima vosproizvedeniya «kartinka-v-kartinke (Analysis and choosing of the digital filters for digital image frames resampling for displaying in “picture-in-picture” mode) // IZVESTIYA SFedU. Engineering sciences, 2012. – no. 5 (130). – PP. 185-189. 4. Malakhin V.A., Tereshin A.A., Goncharov S.N., Pisetskiy V.V., Goncharov E.S. Issledovanie i modelirovanie algoritmov vosstanovleniya cifrovogo signala mezhdu otschetami (Research and modeling of algorithms for recovering a digital signal between samples) // Trudy mezhdunarodnogo simpoziuma «Nadezhnost' i kachestvo», 2018. – Vol. 1. – PP. 344-349. 5. Mikheev S. Å., Morozov P. D. Primenenie kvaziermitovyh kubicheskih splajnov dlya perediskretizacii zvukovyh fajlov (Application of quasihermitian cubic splines for oversampling of audio files) // Transactions of Karelian Research Centre of Russian Academy of Science. No 4. Mathematical Modeling and Information Technologies. 2014. Pp. 106-115. 6. Spazhakin M. I. Primenenie mnogokanal'nyh resemplerov farrou v zadachah radiomonitoringa (Application of multichannel farrow resamplers in radio monitoring tasks) // RadioEngineering, – 2018. No. 7. – Pp. 29-34. DOI: 10.18127/j00338486-201807-06. 7. Petukhov K. Yu., Shayakhmetov M. R. Perediskretizaciya kak metod bor'by s shumom (Rediscretizating as a method of anti-noise) // Vestnik KIGIT, 2012, no. 7 (25), pp. 4-8. 8. Cheng X., et al. Efficient L0 resampling of point sets. Comput. Aided Geom. Des. (2019), 101790. DOI: https://doi.org/10.1016/j.cagd.2019.101790. 9. Vvedenie v konturnyj analiz i ego prilozhenie k obrabotke izobrazhenij i signalov (Contour analysis introduction and its image and signal processing application) / edited by Ya.A. Furman – Moscow: Fizmatlit, 2002. – 592 p. 10. Sergienko A.B. Cifrovaya obrabotka signalov: ucheb. posobie (Digital Signal Processing: Tutoria). 3-ed – SPb.: BHV-Peterburg, 2011. 11. Osnovy teorii obrabotki nepreryvnyh konturov izobrazhenij (Fundamentals of the theory of processing continuous contours of images: monograph) / edited by R.G. Khafizov – Yoshkar-Ola: VSUT, Volga Tech, 2015, 171 p. ISBN 978-5-8158-1606-0. 12. Khafizov R.G., Okhotnikov S.A. Raspoznavanie nepreryvnyh kompleksnoznachnyh konturov izobrazhenij (Recognition of continuous complex-valued image contours) // Izvestiya vysshikh uchebnykh zavedeniy. Priborostroenie, 2012, no. 5, pp. 3-9. 13. Khafizov R.G., Okhotnikov S.A. Diskretizaciya nepreryvnyh konturov izobrazhenij, zadannyh v kompleksnoznachnom vide (Discretization of continuous contours of images, defined in a complex-valued form) // Computer Optics 2018, vol. 36, no. 2, pp. 274-278.
In terms of the ratio calculation speed / proportion of excessively rejected the same name objects, the algorithm for constructing a mask of low-informative areas based on difference of Gaussians has proven to be optimal. The sample size of successfully identified the same name objects in most cases is sufficient to solve the problem. Also, it is comparable with the standard algorithm but provides significantly better performance. Unfortunately, all the considered algorithms for determining low-informative areas showed unsatisfactory results in the problem of detecting cloud objects. In this regard, it is recommended to use specialized solutions for detecting cloud objects. The issue of fast rejection of cloud objects is planned to be considered in the next work. 2. Asmus V.V., Bunchev A.A., Pjatkin V.P. Klasternyj analiz v obrabotke dannyh distancionnogo zondirovanija Zemli // Interjekspo GEO-Sibir', 2015, pp. 71-78. 3. Vetrov A.A., Kuznetsov A.E. Avtomaticheskaja segmentacija oblachnyh obektov na snimkah zemnoj poverhnosti vysokogo prostranstvennogo razreshenija // Issledovanija Zemli iz kosmosa, 2014, pp. 27-34. 4. Astafurov V.G., Skorohodov A.V. Segmentacija sputnikovyh snimkov oblachnosti po teksturnym priznakam na osnove nejrosetevyh tehnologij // Issledovanie Zemli iz kosmosa, 2011, no. 6, pp. 10-20. 5. Gonzalez R., Woods R. Cifrovaja obrabotka izobrazhenij. –Tehnosfera, 2000, 1072 s. 6. Bay H., Tuytelaars T., Van Gool L. SURF: Speeded Up Robust Features / Lecture Notes in Computer Science, 2006, vol 3951. Springer, Berlin, Heidelberg. 7. Chandelier L., Coeurdeve L., Bosc S., Fave P., Gachet R., Orsoni A., Tilak T., Barot A. A worldwide 3d GCP database inherited from 20 years of massive multi-satellite observations // ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-2-2020, pp. 15–23.
The method uses the following parameters: Earth parameters, apogee and perigee of the satellite orbit, look angle, spacecraft measurement equipment accuracy (the number, precision and placement of star trackers, precision of angular velocity sensors), interior orientation parameters (focus, pixel size, distance between rows of even and odd CCDs, photozone width, CCD position estimation accuracy), accuracy of DEM, satellite vibration amplitude, geometric processing accuracy. The error of the satellite spatial position determining is assumed to be negligible to affect the stitching. The displacement of images from neighboring CCDs (scans) have a complex form. It is caused by the fact that one ground point is shot by neighboring matrices with a certain time interval. During this time, the orientation of the spacecraft changes, the Earth rotates, and the spacecraft moves in orbit. Due to movement along the orbit, among other things, parallactic distortions between scans appears. To estimate the total stitching error, the orbital velocity of the satellite and the interval between shooting one point by neighboring CCDs are first calculated. Using this interval, the influence of the angular velocity measurement random error on the stitching is determined. Then, the total accuracy of the spacecraft orientation angles measuring using star trackers is found, taking into account star trackers relative position. Using the orientation measurement error, the error of the angular velocity sensors “drift” and the stitching error caused by it are found. Next, the stitching errors due to DEM error, CCD placement error and geometric processing error are calculated. Stitching error due to vibrations is then evaluated. Stitching error due to vibrations of a known frequency and amplitude, which may not be foreseen in advance, is considered separately; such distortions was detected in the “Aist-2D” small spacecraft images using spectral analysis. Finally, the errors from all considered factors are summarized. The method has been tested on real images from “Aist-2D” small spacecraft. The test results confirmed the adequacy of the proposed model. 2. Kuznetcov A.E., Presniakov O.A., Myatov G.N. Stitching of remote sensing images from staggered TDI CCD. “Digital Signal Processing”. 2015. ¹ 3. P. 29–36. 3. Tang X.; Hu F.; Wang M.; Pan J.; Jin S.; Lu G. Inner FoV Stitching of Spaceborne TDI CCD Images Based on Sensor Geometry and Projection Plane in Object Space. Remote Sens. 2014, 6, 6386-6406. 4. Kirilin A.N., Akhmetov, Shakhmatov E.V. et al. Technology small AIST-2D spacecraft. – Samara: Publishing house SamNZ RAN, 2017. 324 P. 5. Kuznetcov A.E., Poshekhonov V. I. Structural and parametric synthesis of cartographic small spacecraft components. RSREU journal. 2019. ¹ 4 (issue 69). 6. Akhmetov R. N., Eremeev V.V., Kuznetcov A.E., Myatov G.N., Poshekhonov V. I., Stratitatov N.R.. Organization of high-precision geolocation of Earth surface images from the Spacecraft “Resurs-P”. Issledovanie Zemli iz Kosmosa. 2017, ¹ 1. 7. Draper N.R., Smith H. Applied regression analysis. 2nd edition. Vol. 1. [Russian translation] Moscow, Finance and statistics, 1986. 366 P. 8. Eremeev V.V. (ed.) Sovremennye tekhnologii obrabotki dannyh distancionnogo zondirovaniya Zemli [Modern technologies for Earth remote sensing data processing]. Moscow. Fizmatlit. 2015 9. Igolkin A.A., Safin A.I., Filipov A.G. Modal analysis of the dynamic mockup of “AIST–2D” small spacecraft. Vestnik Samarskogo universiteta. Aerokosmicheskaya tekhnika, tekhnologii i mashinostroenie. [Bulletin of the Samara University. Aerospace engineering, technology and mechanical engineering] 2018. vol. 17, ¹ 2. P. 100–108. 10. Shortridge A., Messina J. Spatial structure and landscape associations of SRTM error. Remote Sens. Environ., 2011, vol. 115, no. 6, pp. 1576-1587. doi: 10.1016/j.rse.2011.02.017.
2. Eremeev V.V., Zinina I.I., Kuznetsov A.E., Myatov G.N., Poshekhonov V.I., Filatov A.V., YUdakov A.A.Tekhnologiya potokovoj obrabotki dannyh DZZ vysokogo razresheniya // Sovremennye problemy distancionnogo zondirovaniya Zemli iz kosmosa. 2021. Ò. 18. ¹ 1. pp. 11–18. 3. Ahmetov R.N., Zinina I.I., Yudakov A.A., Eremeev V.V., Kuznetsov A.E., Poshekhonov V.I., Presnyakov O.A., Svetelkin P.N. Tochnostnye harakteristiki vyhodnoj produkcii vysokogo razresheniya KA «Resurs-P» // Sovremennye problemy distancionnogo zondirovaniya Zemli iz kosmosa. 2020. Ò. 17. ¹ 3. pp. 41–47. 4. Greslou D., de Lussy F., Delvit J.M., Dechoz C., Amberg V. Pleiades-HR innovative techniques for Geometric Image Quality Commissioning // ISPRS Melbourne. 2012. |